DeepLearning V1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48a4b739",
   "metadata": {},
   "source": [
    "# 1. Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "0d9a99d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, multilabel_confusion_matrix\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6206691b",
   "metadata": {},
   "source": [
    "# 2. Check the coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "61b22970",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bench = pd.read_csv('./labeling/benchpress_coords.csv')\n",
    "df_dead = pd.read_csv('./labeling/deadlift_coords.csv')\n",
    "df_squat = pd.read_csv('./labeling/squat_coords.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "098732e0",
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "6ca142bf",
   "metadata": {},
   "outputs": [
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       "      <th>3</th>\n",
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       "            class        x1        y1        z1        v1        x2        y2  \\\n",
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       "2  d_correct_down  0.603379  0.289000  0.876913  0.977582  0.599490  0.280128   \n",
       "3    d_correct_up  0.704687  0.148774  0.440767  0.995209  0.700908  0.139350   \n",
       "4    d_correct_up  0.716225  0.146869  0.293682  0.998649  0.713821  0.136261   \n",
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       "4  0.216470  0.998652  0.709841  ...  0.414867  0.955359  0.579698  0.642973   \n",
       "\n",
       "        z32       v32       x33       y33       z33       v33  \n",
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       "4  0.006126  0.938232  0.745315  0.636252  0.365578  0.844092  \n",
       "\n",
       "[5 rows x 133 columns]"
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     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_dead.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "fe058cdc",
   "metadata": {},
   "outputs": [
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       "      <th>v31</th>\n",
       "      <th>x32</th>\n",
       "      <th>y32</th>\n",
       "      <th>z32</th>\n",
       "      <th>v32</th>\n",
       "      <th>x33</th>\n",
       "      <th>y33</th>\n",
       "      <th>z33</th>\n",
       "      <th>v33</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s_spine_neutral_down</td>\n",
       "      <td>0.578854</td>\n",
       "      <td>0.459936</td>\n",
       "      <td>-0.178820</td>\n",
       "      <td>0.999752</td>\n",
       "      <td>0.594528</td>\n",
       "      <td>0.448642</td>\n",
       "      <td>-0.173299</td>\n",
       "      <td>0.999830</td>\n",
       "      <td>0.594286</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.313403</td>\n",
       "      <td>0.976089</td>\n",
       "      <td>0.302474</td>\n",
       "      <td>0.597096</td>\n",
       "      <td>0.656582</td>\n",
       "      <td>0.806852</td>\n",
       "      <td>0.332788</td>\n",
       "      <td>0.694722</td>\n",
       "      <td>-0.373114</td>\n",
       "      <td>0.985341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s_spine_neutral_down</td>\n",
       "      <td>0.590740</td>\n",
       "      <td>0.410155</td>\n",
       "      <td>-0.341654</td>\n",
       "      <td>0.999727</td>\n",
       "      <td>0.595270</td>\n",
       "      <td>0.397050</td>\n",
       "      <td>-0.327615</td>\n",
       "      <td>0.999847</td>\n",
       "      <td>0.594844</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.305247</td>\n",
       "      <td>0.975016</td>\n",
       "      <td>0.359256</td>\n",
       "      <td>0.611075</td>\n",
       "      <td>0.285869</td>\n",
       "      <td>0.773935</td>\n",
       "      <td>0.334483</td>\n",
       "      <td>0.695383</td>\n",
       "      <td>-0.373157</td>\n",
       "      <td>0.984979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s_spine_neutral_up</td>\n",
       "      <td>0.572588</td>\n",
       "      <td>0.146920</td>\n",
       "      <td>-0.468999</td>\n",
       "      <td>0.999881</td>\n",
       "      <td>0.573966</td>\n",
       "      <td>0.125410</td>\n",
       "      <td>-0.442385</td>\n",
       "      <td>0.999909</td>\n",
       "      <td>0.573949</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.462458</td>\n",
       "      <td>0.976983</td>\n",
       "      <td>0.493459</td>\n",
       "      <td>0.641891</td>\n",
       "      <td>0.653005</td>\n",
       "      <td>0.840647</td>\n",
       "      <td>0.369003</td>\n",
       "      <td>0.695396</td>\n",
       "      <td>-0.618573</td>\n",
       "      <td>0.986593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s_spine_neutral_up</td>\n",
       "      <td>0.563577</td>\n",
       "      <td>0.157547</td>\n",
       "      <td>-0.483738</td>\n",
       "      <td>0.999883</td>\n",
       "      <td>0.564866</td>\n",
       "      <td>0.136710</td>\n",
       "      <td>-0.457015</td>\n",
       "      <td>0.999882</td>\n",
       "      <td>0.564586</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.401422</td>\n",
       "      <td>0.968094</td>\n",
       "      <td>0.488726</td>\n",
       "      <td>0.641720</td>\n",
       "      <td>0.591863</td>\n",
       "      <td>0.822999</td>\n",
       "      <td>0.332679</td>\n",
       "      <td>0.700715</td>\n",
       "      <td>-0.569745</td>\n",
       "      <td>0.981765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s_spine_neutral_down</td>\n",
       "      <td>0.579107</td>\n",
       "      <td>0.414149</td>\n",
       "      <td>-0.166554</td>\n",
       "      <td>0.999601</td>\n",
       "      <td>0.593653</td>\n",
       "      <td>0.400882</td>\n",
       "      <td>-0.152463</td>\n",
       "      <td>0.999702</td>\n",
       "      <td>0.593684</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.601359</td>\n",
       "      <td>0.977251</td>\n",
       "      <td>0.393079</td>\n",
       "      <td>0.617481</td>\n",
       "      <td>0.678957</td>\n",
       "      <td>0.802303</td>\n",
       "      <td>0.347247</td>\n",
       "      <td>0.690384</td>\n",
       "      <td>-0.761584</td>\n",
       "      <td>0.985078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 133 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  class        x1        y1        z1        v1        x2  \\\n",
       "0  s_spine_neutral_down  0.578854  0.459936 -0.178820  0.999752  0.594528   \n",
       "1  s_spine_neutral_down  0.590740  0.410155 -0.341654  0.999727  0.595270   \n",
       "2    s_spine_neutral_up  0.572588  0.146920 -0.468999  0.999881  0.573966   \n",
       "3    s_spine_neutral_up  0.563577  0.157547 -0.483738  0.999883  0.564866   \n",
       "4  s_spine_neutral_down  0.579107  0.414149 -0.166554  0.999601  0.593653   \n",
       "\n",
       "         y2        z2        v2        x3  ...       z31       v31       x32  \\\n",
       "0  0.448642 -0.173299  0.999830  0.594286  ... -0.313403  0.976089  0.302474   \n",
       "1  0.397050 -0.327615  0.999847  0.594844  ... -0.305247  0.975016  0.359256   \n",
       "2  0.125410 -0.442385  0.999909  0.573949  ... -0.462458  0.976983  0.493459   \n",
       "3  0.136710 -0.457015  0.999882  0.564586  ... -0.401422  0.968094  0.488726   \n",
       "4  0.400882 -0.152463  0.999702  0.593684  ... -0.601359  0.977251  0.393079   \n",
       "\n",
       "        y32       z32       v32       x33       y33       z33       v33  \n",
       "0  0.597096  0.656582  0.806852  0.332788  0.694722 -0.373114  0.985341  \n",
       "1  0.611075  0.285869  0.773935  0.334483  0.695383 -0.373157  0.984979  \n",
       "2  0.641891  0.653005  0.840647  0.369003  0.695396 -0.618573  0.986593  \n",
       "3  0.641720  0.591863  0.822999  0.332679  0.700715 -0.569745  0.981765  \n",
       "4  0.617481  0.678957  0.802303  0.347247  0.690384 -0.761584  0.985078  \n",
       "\n",
       "[5 rows x 133 columns]"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_squat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c03291a0",
   "metadata": {},
   "source": [
    "# 3. Data Preprocessing & Data Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "id": "a4debbf8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 데이터 로드\n",
    "bench_data = np.loadtxt(\"./labeling/benchpress_coords.csv\", delimiter=\",\", dtype=str, skiprows=1)\n",
    "labels = bench_data[:, 0]\n",
    "bench_data = bench_data[:, 1:]  # 첫 번째 열을 제외한 모든 열 선택\n",
    "\n",
    "# One-Hot Encoding\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "labels = encoder.fit_transform(labels.reshape(-1, 1))\n",
    "\n",
    "# 결측값 처리\n",
    "bench_data = np.nan_to_num(bench_data)\n",
    "\n",
    "bench_data = bench_data.astype(float)\n",
    "\n",
    "# 데이터 분할\n",
    "bench_X_train, bench_X_test, bench_y_train, bench_y_test = train_test_split(bench_data, labels, test_size=0.25)\n",
    "\n",
    "# 텐서 변환\n",
    "bench_X_train = torch.tensor(bench_X_train)\n",
    "bench_X_test = torch.tensor(bench_X_test)\n",
    "bench_y_train = torch.tensor(bench_y_train)\n",
    "bench_y_test = torch.tensor(bench_y_test)\n",
    "\n",
    "# float 변환\n",
    "bench_X_train = bench_X_train.float()\n",
    "bench_X_test = bench_X_test.float()\n",
    "bench_y_train = bench_y_train.float()\n",
    "bench_y_test = bench_y_test.float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "e36cffe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 로드\n",
    "squat_data = np.loadtxt(\"./labeling/squat_coords.csv\", delimiter=\",\", dtype=str, skiprows=1)\n",
    "labels_squat = squat_data[:, 0]\n",
    "squat_data = squat_data[:, 1:]  # 첫 번째 열을 제외한 모든 열 선택\n",
    "\n",
    "# One-Hot Encoding\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "labels_squat = encoder.fit_transform(labels_squat.reshape(-1, 1))\n",
    "\n",
    "# 결측값 처리\n",
    "squat_data = np.nan_to_num(squat_data)\n",
    "\n",
    "squat_data = squat_data.astype(float)\n",
    "\n",
    "# 데이터 분할\n",
    "squat_X_train, squat_X_test, squat_y_train, squat_y_test = train_test_split(squat_data, labels_squat, test_size=0.25)\n",
    "\n",
    "# 텐서 변환\n",
    "squat_X_train = torch.tensor(squat_X_train)\n",
    "squat_X_test = torch.tensor(squat_X_test)\n",
    "squat_y_train = torch.tensor(squat_y_train)\n",
    "squat_y_test = torch.tensor(squat_y_test)\n",
    "\n",
    "# float 변환\n",
    "squat_X_train = squat_X_train.float()\n",
    "squat_X_test = squat_X_test.float()\n",
    "squat_y_train = squat_y_train.float()\n",
    "squat_y_test = squat_y_test.float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "daf4f7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 로드\n",
    "deadlift_data = np.loadtxt(\"./labeling/deadlift_coords.csv\", delimiter=\",\", dtype=str, skiprows=1)\n",
    "labels_deadlift = deadlift_data[:, 0]\n",
    "deadlift_data = deadlift_data[:, 1:]  # 첫 번째 열을 제외한 모든 열 선택\n",
    "\n",
    "# One-Hot Encoding\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "labels_deadlift = encoder.fit_transform(labels_deadlift.reshape(-1, 1))\n",
    "\n",
    "# 결측값 처리\n",
    "deadlift_data = np.nan_to_num(deadlift_data)\n",
    "\n",
    "deadlift_data = deadlift_data.astype(float)\n",
    "\n",
    "# 데이터 분할\n",
    "deadlift_X_train, deadlift_X_test, deadlift_y_train, deadlift_y_test = train_test_split(deadlift_data, labels_deadlift, test_size=0.25)\n",
    "\n",
    "# 텐서 변환\n",
    "deadlift_X_train = torch.tensor(deadlift_X_train)\n",
    "deadlift_X_test = torch.tensor(deadlift_X_test)\n",
    "deadlift_y_train = torch.tensor(deadlift_y_train)\n",
    "deadlift_y_test = torch.tensor(deadlift_y_test)\n",
    "\n",
    "# float 변환\n",
    "deadlift_X_train = deadlift_X_train.float()\n",
    "deadlift_X_test = deadlift_X_test.float()\n",
    "deadlift_y_train = deadlift_y_train.float()\n",
    "deadlift_y_test = deadlift_y_test.float()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3122dfa3",
   "metadata": {},
   "source": [
    "# 4. Construct Deep Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "b5c55c18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([239, 6])"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bench_y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "53c59927",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([716, 6])"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bench_y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d56f3fb",
   "metadata": {},
   "source": [
    "### Bench Press"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "85728780",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DeepClassifier(nn.Module):\n",
    "    def __init__(self, input_size, output_size):\n",
    "        super(DeepClassifier, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_size, 128)\n",
    "        self.fc2 = nn.Linear(128, 64)\n",
    "        self.fc3 = nn.Linear(64, 32)\n",
    "        self.fc4 = nn.Linear(32, output_size)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.dropout(self.relu(self.fc1(x)))\n",
    "        x = self.dropout(self.relu(self.fc2(x)))\n",
    "        x = self.dropout(self.relu(self.fc3(x)))\n",
    "        x = self.fc4(x)\n",
    "        x = self.softmax(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "id": "30ab9554",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 생성\n",
    "input_size = 132\n",
    "output_size = 6\n",
    "bench_model = DeepClassifier(input_size, output_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "afdac53a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bench_criterion = nn.BCELoss()\n",
    "bench_optimizer = optim.Adam(bench_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "0a7e3c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bench_train_losses = []\n",
    "bench_test_losses = []\n",
    "\n",
    "epochs = 1000\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    bench_model.train()\n",
    "    bench_optimizer.zero_grad()\n",
    "    bench_outputs = bench_model(bench_X_train)\n",
    "    bench_loss = bench_criterion(bench_outputs, bench_y_train)\n",
    "    bench_loss.backward()\n",
    "    bench_optimizer.step()\n",
    "    bench_train_losses.append(bench_loss.item())\n",
    "    \n",
    "    bench_model.eval()\n",
    "    with torch.no_grad():\n",
    "        bench_test_outputs = bench_model(bench_X_test)\n",
    "        bench_test_loss = bench_criterion(bench_test_outputs, bench_y_test)\n",
    "        bench_test_losses.append(bench_test_loss.item())\n",
    "\n",
    "plt.plot(range(1, epochs+1), bench_train_losses, label='Train Loss')\n",
    "plt.plot(range(1, epochs+1), bench_test_losses, label='Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "fbca1495",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.09512793272733688\n"
     ]
    }
   ],
   "source": [
    "bench_model.eval()\n",
    "with torch.no_grad():\n",
    "    test_outputs = bench_model(bench_X_test)\n",
    "    test_loss = bench_criterion(test_outputs, bench_y_test)\n",
    "    print(f'Test Loss: {test_loss.item()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "8bcb0bfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 0.3866,  0.3080,  0.4083,  1.0000,  0.3746,  0.3029,  0.4775,  1.0000,\n",
      "         0.3759,  0.3024,  0.4775,  1.0000,  0.3768,  0.3023,  0.4772,  1.0000,\n",
      "         0.3645,  0.3079,  0.4289,  1.0000,  0.3586,  0.3110,  0.4289,  1.0000,\n",
      "         0.3525,  0.3145,  0.4289,  1.0000,  0.3699,  0.3134,  0.7604,  1.0000,\n",
      "         0.3382,  0.3288,  0.5128,  1.0000,  0.3976,  0.3160,  0.5087,  1.0000,\n",
      "         0.3881,  0.3209,  0.4412,  1.0000,  0.4742,  0.3421,  0.7964,  1.0000,\n",
      "         0.3424,  0.3840,  0.3744,  1.0000,  0.6146,  0.3807,  0.7346,  0.1243,\n",
      "         0.3117,  0.4480, -0.1171,  0.9848,  0.6689,  0.3557,  0.4690,  0.3026,\n",
      "         0.3142,  0.3601, -0.5110,  0.9647,  0.7054,  0.3615,  0.4296,  0.3278,\n",
      "         0.3190,  0.3485, -0.5975,  0.9413,  0.6988,  0.3506,  0.3808,  0.3307,\n",
      "         0.3217,  0.3463, -0.5216,  0.9219,  0.6694,  0.3496,  0.4387,  0.3209,\n",
      "         0.3206,  0.3514, -0.4891,  0.8860,  0.7177,  0.4070,  0.1354,  1.0000,\n",
      "         0.6257,  0.4406, -0.1357,  1.0000,  0.9781,  0.4865,  0.0192,  0.8995,\n",
      "         0.7321,  0.5701, -0.1544,  0.9756,  0.8449,  0.5082,  0.7652,  0.1891,\n",
      "         0.5736,  0.5813,  0.4483,  0.4806,  0.7723,  0.5044,  0.8545,  0.3933,\n",
      "         0.5410,  0.5666,  0.5252,  0.5745,  0.8211,  0.5325,  0.9438,  0.3194,\n",
      "         0.5524,  0.6228,  0.5738,  0.5373])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 1., 0.])\n",
      "예측된 데이터: tensor(4)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0000', '1.0000', '0.0000']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 5.0887e-01,  3.5894e-01,  6.7035e-01,  1.0000e+00,  5.1776e-01,\n",
      "         3.5834e-01,  7.2117e-01,  1.0000e+00,  5.2534e-01,  3.5991e-01,\n",
      "         7.2162e-01,  1.0000e+00,  5.3300e-01,  3.6188e-01,  7.2136e-01,\n",
      "         1.0000e+00,  4.9833e-01,  3.5746e-01,  7.3300e-01,  1.0000e+00,\n",
      "         4.9157e-01,  3.5823e-01,  7.3306e-01,  1.0000e+00,  4.8459e-01,\n",
      "         3.5939e-01,  7.3319e-01,  1.0000e+00,  5.4430e-01,  3.7251e-01,\n",
      "         7.9554e-01,  1.0000e+00,  4.7316e-01,  3.7005e-01,  8.5315e-01,\n",
      "         1.0000e+00,  5.2436e-01,  3.6886e-01,  6.7995e-01,  1.0000e+00,\n",
      "         4.9698e-01,  3.6737e-01,  6.9486e-01,  1.0000e+00,  6.1648e-01,\n",
      "         4.1473e-01,  7.1358e-01,  9.9998e-01,  4.0625e-01,  4.1508e-01,\n",
      "         7.4592e-01,  9.9999e-01,  7.5102e-01,  4.0266e-01,  8.3935e-01,\n",
      "         9.7833e-01,  2.7740e-01,  4.0104e-01,  7.5705e-01,  9.9853e-01,\n",
      "         7.8751e-01,  3.2963e-01,  1.0221e+00,  9.8799e-01,  2.4488e-01,\n",
      "         3.3179e-01,  7.8959e-01,  9.9918e-01,  7.9020e-01,  3.1274e-01,\n",
      "         1.0621e+00,  9.6944e-01,  2.4275e-01,  3.1152e-01,  7.9201e-01,\n",
      "         9.9720e-01,  7.7774e-01,  3.1162e-01,  1.0730e+00,  9.6284e-01,\n",
      "         2.6193e-01,  3.1072e-01,  8.1168e-01,  9.9563e-01,  7.7429e-01,\n",
      "         3.1740e-01,  1.0255e+00,  9.6451e-01,  2.6352e-01,  3.1635e-01,\n",
      "         7.9090e-01,  9.9518e-01,  5.8610e-01,  5.0836e-01, -6.6899e-04,\n",
      "         9.9997e-01,  4.1430e-01,  5.0918e-01,  4.8020e-05,  9.9999e-01,\n",
      "         7.8079e-01,  5.7948e-01, -4.4574e-01,  9.9586e-01,  1.9875e-01,\n",
      "         5.6311e-01, -4.2597e-01,  9.9678e-01,  7.5350e-01,  6.8474e-01,\n",
      "        -1.0115e-02,  8.8668e-01,  2.6377e-01,  6.7344e-01, -3.3235e-02,\n",
      "         9.8339e-01,  7.1521e-01,  6.9800e-01,  2.9149e-02,  9.1468e-01,\n",
      "         2.9881e-01,  6.8649e-01,  6.2552e-03,  9.6739e-01,  8.1499e-01,\n",
      "         7.4367e-01, -1.2247e-01,  9.0551e-01,  1.8814e-01,  7.3876e-01,\n",
      "        -1.5753e-01,  9.6677e-01])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 1., 0., 0.])\n",
      "예측된 데이터: tensor(3)\n",
      "예측 확률: ['0.0087', '0.2250', '0.0007', '0.7515', '0.0001', '0.0140']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 0.3424,  0.2932,  0.6157,  1.0000,  0.3339,  0.2932,  0.6624,  1.0000,\n",
      "         0.3350,  0.2951,  0.6626,  1.0000,  0.3361,  0.2971,  0.6625,  1.0000,\n",
      "         0.3275,  0.2931,  0.5970,  1.0000,  0.3247,  0.2948,  0.5970,  1.0000,\n",
      "         0.3221,  0.2967,  0.5970,  1.0000,  0.3340,  0.3146,  0.8421,  1.0000,\n",
      "         0.3127,  0.3150,  0.5490,  1.0000,  0.3508,  0.3056,  0.6884,  1.0000,\n",
      "         0.3447,  0.3047,  0.6015,  1.0000,  0.4411,  0.3386,  0.9536,  1.0000,\n",
      "         0.2997,  0.3529,  0.2905,  1.0000,  0.5426,  0.2606,  1.0943,  0.6605,\n",
      "         0.3077,  0.2728, -0.0432,  0.9965,  0.5774,  0.1893,  1.1759,  0.7962,\n",
      "         0.3332,  0.1792, -0.0614,  0.9948,  0.5880,  0.1683,  1.2000,  0.7789,\n",
      "         0.3496,  0.1585, -0.1240,  0.9867,  0.5763,  0.1649,  1.1536,  0.7780,\n",
      "         0.3520,  0.1570, -0.0191,  0.9836,  0.5704,  0.1717,  1.1516,  0.7842,\n",
      "         0.3506,  0.1638, -0.0212,  0.9775,  0.6891,  0.4084,  0.1707,  1.0000,\n",
      "         0.5793,  0.4269, -0.1709,  1.0000,  0.9678,  0.4363, -0.1950,  0.9950,\n",
      "         0.6769,  0.5028, -0.6515,  0.9993,  0.8723,  0.5680, -0.0069,  0.9790,\n",
      "         0.6719,  0.6612, -0.8722,  0.9985,  0.8402,  0.5839,  0.0109,  0.9631,\n",
      "         0.6351,  0.6710, -0.8928,  0.9947,  0.9798,  0.6118, -0.0420,  0.9753,\n",
      "         0.7253,  0.7387, -1.0202,  0.9979])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 1., 0., 0.])\n",
      "예측된 데이터: tensor(3)\n",
      "예측 확률: ['0.0049', '0.2571', '0.0002', '0.7328', '0.0000', '0.0050']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 0.5142,  0.3626,  0.8236,  1.0000,  0.5221,  0.3649,  0.8709,  1.0000,\n",
      "         0.5295,  0.3664,  0.8714,  1.0000,  0.5371,  0.3684,  0.8710,  1.0000,\n",
      "         0.5033,  0.3641,  0.8858,  1.0000,  0.4975,  0.3649,  0.8858,  1.0000,\n",
      "         0.4914,  0.3659,  0.8861,  1.0000,  0.5469,  0.3803,  0.9134,  1.0000,\n",
      "         0.4793,  0.3769,  0.9843,  1.0000,  0.5285,  0.3700,  0.8193,  1.0000,\n",
      "         0.5031,  0.3684,  0.8377,  1.0000,  0.6189,  0.4187,  0.7848,  1.0000,\n",
      "         0.4029,  0.4160,  0.8641,  1.0000,  0.7509,  0.3914,  0.8922,  0.9693,\n",
      "         0.2856,  0.3838,  0.9470,  0.9969,  0.7874,  0.3178,  1.0853,  0.9808,\n",
      "         0.2443,  0.3121,  1.0895,  0.9989,  0.7887,  0.3003,  1.1296,  0.9523,\n",
      "         0.2436,  0.2918,  1.1044,  0.9961,  0.7723,  0.3019,  1.1439,  0.9442,\n",
      "         0.2607,  0.2926,  1.1208,  0.9941,  0.7687,  0.3080,  1.0807,  0.9514,\n",
      "         0.2626,  0.2997,  1.0884,  0.9943,  0.5862,  0.5054,  0.0028,  1.0000,\n",
      "         0.4131,  0.5096, -0.0033,  1.0000,  0.7939,  0.5766, -0.4946,  0.9971,\n",
      "         0.1931,  0.5636, -0.4390,  0.9970,  0.7544,  0.6834, -0.1036,  0.9016,\n",
      "         0.2606,  0.6722, -0.0979,  0.9858,  0.7124,  0.6981, -0.0673,  0.9303,\n",
      "         0.2973,  0.6851, -0.0645,  0.9660,  0.8219,  0.7409, -0.2189,  0.9104,\n",
      "         0.1916,  0.7378, -0.2359,  0.9715])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 1., 0., 0.])\n",
      "예측된 데이터: tensor(3)\n",
      "예측 확률: ['0.0046', '0.3941', '0.0001', '0.5999', '0.0000', '0.0013']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 0.4983,  0.3725,  0.8266,  1.0000,  0.5064,  0.3743,  0.8876,  1.0000,\n",
      "         0.5139,  0.3757,  0.8879,  1.0000,  0.5220,  0.3775,  0.8875,  1.0000,\n",
      "         0.4882,  0.3741,  0.9002,  1.0000,  0.4821,  0.3751,  0.9002,  1.0000,\n",
      "         0.4761,  0.3764,  0.9003,  1.0000,  0.5334,  0.3891,  0.9875,  1.0000,\n",
      "         0.4640,  0.3878,  1.0444,  1.0000,  0.5124,  0.3793,  0.8448,  1.0000,\n",
      "         0.4879,  0.3789,  0.8591,  1.0000,  0.6112,  0.4266,  0.8397,  1.0000,\n",
      "         0.3896,  0.4287,  0.9369,  1.0000,  0.7464,  0.4083,  0.7588,  0.9576,\n",
      "         0.2615,  0.4091,  0.8230,  0.9969,  0.7609,  0.3293,  0.7956,  0.9578,\n",
      "         0.2328,  0.3404,  0.7169,  0.9979,  0.7530,  0.3134,  0.8002,  0.9050,\n",
      "         0.2261,  0.3228,  0.6920,  0.9949,  0.7406,  0.3125,  0.8327,  0.8847,\n",
      "         0.2481,  0.3222,  0.7245,  0.9923,  0.7399,  0.3178,  0.8022,  0.9093,\n",
      "         0.2515,  0.3262,  0.7181,  0.9919,  0.5868,  0.5098, -0.0205,  1.0000,\n",
      "         0.4275,  0.5257,  0.0204,  1.0000,  0.7065,  0.5795, -0.3740,  0.9934,\n",
      "         0.2628,  0.6528, -0.5630,  0.9937,  0.6774,  0.6306, -0.0221,  0.4026,\n",
      "         0.4097,  0.6502, -0.1219,  0.2777,  0.6515,  0.6280,  0.0183,  0.7587,\n",
      "         0.4422,  0.6463, -0.0691,  0.6091,  0.7157,  0.6852, -0.0271,  0.7180,\n",
      "         0.4069,  0.6766, -0.1075,  0.5364])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 1.])\n",
      "예측된 데이터: tensor(5)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0000', '0.0006', '0.9994']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_11625/3089623518.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "# 테스트 데이터셋에서 샘플 선택\n",
    "num_samples_to_test = 5  # 확인할 샘플의 수\n",
    "for i in range(num_samples_to_test):\n",
    "    sample_index = i\n",
    "    input_sample = bench_X_test[sample_index]  # 입력 데이터\n",
    "    target_sample = bench_y_test[sample_index]  # 실제 타겟(정답) 데이터\n",
    "\n",
    "    # 입력 데이터를 Tensor로 변환\n",
    "    input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n",
    "\n",
    "    # 모델에 입력하여 예측 생성\n",
    "    bench_model.eval()\n",
    "    with torch.no_grad():\n",
    "        prediction = bench_model(input_tensor.unsqueeze(0))  # unsqueeze를 사용하여 배치 차원 추가\n",
    "\n",
    "    # 예측 결과 출력\n",
    "    print(f\"샘플 {sample_index + 1}:\")\n",
    "    print(\"입력 데이터:\", input_sample)\n",
    "    print(\"실제 타겟 데이터:\", target_sample)\n",
    "    print(\"예측된 데이터:\", torch.argmax(prediction))  # 확률이 아닌 예측된 클래스를 출력\n",
    "    probabilities = prediction.squeeze().tolist()  # 텐서를 리스트로 변환\n",
    "    probabilities_str = [f\"{prob:.4f}\" for prob in probabilities]\n",
    "    print(\"예측 확률:\", probabilities_str)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "id": "9cacd475",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.766\n",
      "정밀도: 0.968\n",
      "재현율: 0.766\n",
      "F1 점수: 0.855\n"
     ]
    }
   ],
   "source": [
    "bench_model.eval()\n",
    "with torch.no_grad():\n",
    "    predicted_probs = bench_model(bench_X_test)\n",
    "\n",
    "threshold = 0.8  # 임계값 설정\n",
    "predicted_labels = (predicted_probs > threshold).numpy().astype(int)\n",
    "\n",
    "# 정확도 계산\n",
    "accuracy = accuracy_score(bench_y_test, predicted_labels)\n",
    "print(f\"정확도: {accuracy:.3f}\")\n",
    "\n",
    "# 정밀도 계산\n",
    "precision = precision_score(bench_y_test, predicted_labels, average='micro')\n",
    "print(f\"정밀도: {precision:.3f}\")\n",
    "\n",
    "# 재현율 계산\n",
    "recall = recall_score(bench_y_test, predicted_labels, average='micro')\n",
    "print(f\"재현율: {recall:.3f}\")\n",
    "\n",
    "# F1 점수 계산\n",
    "f1 = f1_score(bench_y_test, predicted_labels, average='micro')\n",
    "print(f\"F1 점수: {f1:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "id": "4414bf86",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x3600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = multilabel_confusion_matrix(bench_y_test, predicted_labels)\n",
    "\n",
    "def plot_multilabel_confusion_matrix(confusion_matrix):\n",
    "    num_classes = len(confusion_matrix)\n",
    "    fig, axes = plt.subplots(nrows=num_classes, ncols=1, figsize=(8, 6 * num_classes))\n",
    "\n",
    "    for i in range(num_classes):\n",
    "        sns.heatmap(confusion_matrix[i], annot=True, cmap=\"Blues\", fmt=\"d\", ax=axes[i])\n",
    "        axes[i].set_title(f\"Class {i+1} Confusion Matrix\")\n",
    "        axes[i].set_xlabel(\"Predicted label\")\n",
    "        axes[i].set_ylabel(\"True label\")\n",
    "        axes[i].set_xticklabels(['True', 'False'])\n",
    "        axes[i].set_yticklabels(['True', 'False'])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 다중 레이블 오차 행렬 시각화\n",
    "plot_multilabel_confusion_matrix(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5fffb7",
   "metadata": {},
   "source": [
    "### Squat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "352f5823",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 생성\n",
    "input_size = 132\n",
    "output_size = 8\n",
    "squat_model = DeepClassifier(input_size, output_size)\n",
    "squat_criterion = nn.BCELoss()\n",
    "squat_optimizer = optim.Adam(squat_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "8a9e4e87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "squat_train_losses = []\n",
    "squat_test_losses = []\n",
    "\n",
    "epochs = 1000\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    squat_model.train()\n",
    "    squat_optimizer.zero_grad()\n",
    "    squat_outputs = squat_model(squat_X_train)\n",
    "    squat_loss = squat_criterion(squat_outputs, squat_y_train)\n",
    "    squat_loss.backward()\n",
    "    squat_optimizer.step()\n",
    "    squat_train_losses.append(squat_loss.item())\n",
    "    \n",
    "    squat_model.eval()\n",
    "    with torch.no_grad():\n",
    "        squat_test_outputs = squat_model(squat_X_test)\n",
    "        squat_test_loss = squat_criterion(squat_test_outputs, squat_y_test)\n",
    "        squat_test_losses.append(squat_test_loss.item())\n",
    "\n",
    "plt.plot(range(1, epochs+1), squat_train_losses, label='Train Loss')\n",
    "plt.plot(range(1, epochs+1), squat_test_losses, label='Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "id": "a269f137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.02452334389090538\n"
     ]
    }
   ],
   "source": [
    "squat_model.eval()\n",
    "with torch.no_grad():\n",
    "    squat_test_outputs = squat_model(squat_X_test)\n",
    "    squat_test_loss = squat_criterion(squat_test_outputs, squat_y_test)\n",
    "    print(f'Test Loss: {squat_test_loss.item()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "id": "1ea2e0eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 0.5495,  0.2535, -0.9265,  1.0000,  0.5631,  0.2411, -0.9050,  1.0000,\n",
      "         0.5707,  0.2405, -0.9049,  1.0000,  0.5771,  0.2400, -0.9051,  1.0000,\n",
      "         0.5389,  0.2418, -0.8915,  1.0000,  0.5315,  0.2416, -0.8917,  1.0000,\n",
      "         0.5253,  0.2417, -0.8917,  1.0000,  0.5871,  0.2418, -0.6800,  1.0000,\n",
      "         0.5219,  0.2455, -0.6194,  1.0000,  0.5659,  0.2628, -0.8364,  1.0000,\n",
      "         0.5381,  0.2634, -0.8191,  1.0000,  0.6596,  0.3017, -0.4837,  1.0000,\n",
      "         0.4701,  0.3049, -0.4149,  1.0000,  0.7459,  0.3768, -0.8550,  0.9965,\n",
      "         0.3808,  0.3719, -0.7010,  0.9977,  0.7053,  0.3068, -1.3763,  0.9910,\n",
      "         0.4029,  0.3079, -1.2431,  0.9960,  0.6976,  0.2928, -1.4820,  0.9745,\n",
      "         0.4063,  0.2908, -1.3501,  0.9859,  0.6910,  0.2837, -1.4531,  0.9688,\n",
      "         0.4148,  0.2847, -1.3299,  0.9821,  0.6884,  0.2889, -1.3763,  0.9356,\n",
      "         0.4184,  0.2911, -1.2493,  0.9664,  0.6114,  0.4514, -0.0075,  0.9998,\n",
      "         0.5048,  0.4481,  0.0073,  0.9999,  0.5987,  0.5610, -0.0741,  0.9948,\n",
      "         0.4951,  0.5596, -0.0367,  0.9954,  0.5760,  0.6540,  0.4632,  0.9886,\n",
      "         0.4817,  0.6514,  0.4630,  0.9945,  0.5602,  0.6657,  0.5022,  0.9388,\n",
      "         0.4980,  0.6634,  0.4993,  0.9354,  0.5970,  0.6950,  0.2473,  0.9820,\n",
      "         0.4567,  0.6914,  0.2274,  0.9894])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(1)\n",
      "예측 확률: ['0.0000', '0.9967', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0033']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 0.5412,  0.2644, -0.5509,  1.0000,  0.5544,  0.2537, -0.5293,  1.0000,\n",
      "         0.5622,  0.2532, -0.5293,  1.0000,  0.5684,  0.2527, -0.5294,  1.0000,\n",
      "         0.5326,  0.2531, -0.5228,  1.0000,  0.5257,  0.2522, -0.5229,  1.0000,\n",
      "         0.5193,  0.2514, -0.5228,  1.0000,  0.5782,  0.2530, -0.3115,  1.0000,\n",
      "         0.5149,  0.2525, -0.2826,  1.0000,  0.5560,  0.2738, -0.4623,  1.0000,\n",
      "         0.5285,  0.2729, -0.4545,  1.0000,  0.6421,  0.3063, -0.1228,  1.0000,\n",
      "         0.4600,  0.3076, -0.1151,  1.0000,  0.7056,  0.3760, -0.3786,  0.9963,\n",
      "         0.3969,  0.3734, -0.3280,  0.9933,  0.7034,  0.3143, -0.8537,  0.9956,\n",
      "         0.3916,  0.3165, -0.8058,  0.9959,  0.7034,  0.2994, -0.9405,  0.9858,\n",
      "         0.3922,  0.2996, -0.8976,  0.9874,  0.6973,  0.2903, -0.9250,  0.9842,\n",
      "         0.3987,  0.2927, -0.8904,  0.9861,  0.6933,  0.2963, -0.8582,  0.9727,\n",
      "         0.4020,  0.2984, -0.8146,  0.9779,  0.5944,  0.4543, -0.0016,  0.9999,\n",
      "         0.4862,  0.4528,  0.0015,  1.0000,  0.6701,  0.5629, -0.0190,  0.9975,\n",
      "         0.4084,  0.5574, -0.0957,  0.9991,  0.6838,  0.6495,  0.4755,  0.9945,\n",
      "         0.3785,  0.6480,  0.2879,  0.9989,  0.6615,  0.6647,  0.5149,  0.9602,\n",
      "         0.3992,  0.6637,  0.3138,  0.9823,  0.7381,  0.6855,  0.3147,  0.9886,\n",
      "         0.3182,  0.6825,  0.0921,  0.9959])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 1., 0., 0.])\n",
      "예측된 데이터: tensor(5)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0002', '0.0002', '0.9996', '0.0000', '0.0000']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 0.5260,  0.3894, -0.7759,  1.0000,  0.5362,  0.3812, -0.7967,  1.0000,\n",
      "         0.5435,  0.3804, -0.7966,  0.9999,  0.5514,  0.3790, -0.7968,  0.9999,\n",
      "         0.5217,  0.3797, -0.7802,  1.0000,  0.5185,  0.3784, -0.7804,  1.0000,\n",
      "         0.5148,  0.3769, -0.7805,  1.0000,  0.5705,  0.3704, -0.7088,  0.9999,\n",
      "         0.5169,  0.3703, -0.6373,  1.0000,  0.5408,  0.3943, -0.7262,  1.0000,\n",
      "         0.5234,  0.3915, -0.7051,  1.0000,  0.6503,  0.3886, -0.4683,  1.0000,\n",
      "         0.4759,  0.3854, -0.4467,  1.0000,  0.7173,  0.4324, -0.6729,  0.9873,\n",
      "         0.3947,  0.4325, -0.6686,  0.9948,  0.7130,  0.4212, -0.9577,  0.9603,\n",
      "         0.4083,  0.4221, -1.0449,  0.9930,  0.7183,  0.4108, -1.0070,  0.9057,\n",
      "         0.4028,  0.4130, -1.0984,  0.9794,  0.7082,  0.4044, -0.9889,  0.9048,\n",
      "         0.4164,  0.4080, -1.0759,  0.9779,  0.7020,  0.4098, -0.9564,  0.8741,\n",
      "         0.4212,  0.4138, -1.0411,  0.9689,  0.6144,  0.4776, -0.0018,  0.9999,\n",
      "         0.5140,  0.4726,  0.0018,  0.9999,  0.6536,  0.5695, -0.2562,  0.9885,\n",
      "         0.4528,  0.5611, -0.2737,  0.9941,  0.6309,  0.6550,  0.0126,  0.9870,\n",
      "         0.4612,  0.6498,  0.0058,  0.9948,  0.6130,  0.6674,  0.0294,  0.9297,\n",
      "         0.4763,  0.6643,  0.0240,  0.9425,  0.6595,  0.6920, -0.1502,  0.9862,\n",
      "         0.4228,  0.6853, -0.1544,  0.9912])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 0., 0., 1.])\n",
      "예측된 데이터: tensor(7)\n",
      "예측 확률: ['0.0048', '0.0741', '0.0343', '0.0123', '0.0014', '0.0044', '0.0007', '0.8680']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 0.5423,  0.3372, -0.9845,  1.0000,  0.5539,  0.3269, -0.9768,  1.0000,\n",
      "         0.5617,  0.3260, -0.9767,  1.0000,  0.5684,  0.3250, -0.9770,  1.0000,\n",
      "         0.5326,  0.3264, -0.9635,  1.0000,  0.5261,  0.3254, -0.9637,  1.0000,\n",
      "         0.5209,  0.3244, -0.9637,  1.0000,  0.5829,  0.3191, -0.7572,  1.0000,\n",
      "         0.5169,  0.3206, -0.7102,  1.0000,  0.5578,  0.3447, -0.8904,  1.0000,\n",
      "         0.5309,  0.3443, -0.8737,  1.0000,  0.6468,  0.3611, -0.4986,  1.0000,\n",
      "         0.4696,  0.3617, -0.4751,  1.0000,  0.7145,  0.4409, -0.7444,  0.9880,\n",
      "         0.3929,  0.4307, -0.7183,  0.9983,  0.6725,  0.3817, -1.1780,  0.9726,\n",
      "         0.4185,  0.3799, -1.1914,  0.9959,  0.6714,  0.3690, -1.2684,  0.9262,\n",
      "         0.4179,  0.3654, -1.2776,  0.9829,  0.6639,  0.3602, -1.2569,  0.9146,\n",
      "         0.4248,  0.3586, -1.2598,  0.9780,  0.6597,  0.3641, -1.1857,  0.8371,\n",
      "         0.4287,  0.3637, -1.1937,  0.9570,  0.6046,  0.4648,  0.0025,  0.9998,\n",
      "         0.5076,  0.4620, -0.0026,  0.9998,  0.5971,  0.5652, -0.2670,  0.9823,\n",
      "         0.4876,  0.5644, -0.1980,  0.9888,  0.5837,  0.6589,  0.0929,  0.9754,\n",
      "         0.4763,  0.6561,  0.1378,  0.9928,  0.5700,  0.6701,  0.1162,  0.9207,\n",
      "         0.4880,  0.6668,  0.1588,  0.9244,  0.5974,  0.6947, -0.1342,  0.9711,\n",
      "         0.4599,  0.6940, -0.0930,  0.9859])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(1)\n",
      "예측 확률: ['0.0001', '0.9503', '0.0000', '0.0001', '0.0000', '0.0000', '0.0000', '0.0494']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 0.5347,  0.4654, -0.6807,  1.0000,  0.5429,  0.4609, -0.7059,  1.0000,\n",
      "         0.5501,  0.4598, -0.7058,  1.0000,  0.5573,  0.4585, -0.7060,  1.0000,\n",
      "         0.5249,  0.4596, -0.7025,  1.0000,  0.5201,  0.4578, -0.7027,  1.0000,\n",
      "         0.5157,  0.4561, -0.7028,  1.0000,  0.5724,  0.4460, -0.6199,  1.0000,\n",
      "         0.5074,  0.4454, -0.6054,  1.0000,  0.5452,  0.4617, -0.6262,  1.0000,\n",
      "         0.5264,  0.4608, -0.6230,  1.0000,  0.6280,  0.4361, -0.4204,  0.9999,\n",
      "         0.4619,  0.4381, -0.4391,  1.0000,  0.6824,  0.4887, -0.6408,  0.9500,\n",
      "         0.3783,  0.4970, -0.6288,  0.9972,  0.6742,  0.4613, -1.0259,  0.9194,\n",
      "         0.3979,  0.4609, -0.9419,  0.9928,  0.6827,  0.4497, -1.0993,  0.8605,\n",
      "         0.3987,  0.4477, -0.9949,  0.9747,  0.6706,  0.4444, -1.0944,  0.8506,\n",
      "         0.4098,  0.4451, -0.9703,  0.9708,  0.6645,  0.4491, -1.0328,  0.7673,\n",
      "         0.4132,  0.4495, -0.9356,  0.9517,  0.5855,  0.5315,  0.0185,  0.9999,\n",
      "         0.4887,  0.5284, -0.0185,  1.0000,  0.7179,  0.5710, -0.3472,  0.9841,\n",
      "         0.3669,  0.5632, -0.4070,  0.9929,  0.6842,  0.6526,  0.0042,  0.9634,\n",
      "         0.3742,  0.6535, -0.0676,  0.9947,  0.6607,  0.6652,  0.0326,  0.9368,\n",
      "         0.3957,  0.6660, -0.0405,  0.9713,  0.7358,  0.6866, -0.1113,  0.9393,\n",
      "         0.3195,  0.6822, -0.1997,  0.9851])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 1., 0., 0., 0.])\n",
      "예측된 데이터: tensor(4)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0023', '0.0000', '0.9976', '0.0000', '0.0000', '0.0001']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_11625/3174289499.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "# 테스트 데이터셋에서 샘플 선택\n",
    "num_samples_to_test = 5  # 확인할 샘플의 수\n",
    "for i in range(num_samples_to_test):\n",
    "    sample_index = i\n",
    "    input_sample = squat_X_test[sample_index]  # 입력 데이터\n",
    "    target_sample = squat_y_test[sample_index]  # 실제 타겟(정답) 데이터\n",
    "\n",
    "    # 입력 데이터를 Tensor로 변환\n",
    "    input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n",
    "\n",
    "    # 모델에 입력하여 예측 생성\n",
    "    squat_model.eval()\n",
    "    with torch.no_grad():\n",
    "        prediction = squat_model(input_tensor.unsqueeze(0))  # unsqueeze를 사용하여 배치 차원 추가\n",
    "\n",
    "    # 예측 결과 출력\n",
    "    print(f\"샘플 {sample_index + 1}:\")\n",
    "    print(\"입력 데이터:\", input_sample)\n",
    "    print(\"실제 타겟 데이터:\", target_sample)\n",
    "    print(\"예측된 데이터:\", torch.argmax(prediction))  # 확률이 아닌 예측된 클래스를 출력\n",
    "    probabilities = prediction.squeeze().tolist()  # 텐서를 리스트로 변환\n",
    "    probabilities_str = [f\"{prob:.4f}\" for prob in probabilities]\n",
    "    print(\"예측 확률:\", probabilities_str)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "id": "7d1e3234",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.892\n",
      "정밀도: 0.995\n",
      "재현율: 0.892\n",
      "F1 점수: 0.941\n"
     ]
    }
   ],
   "source": [
    "squat_model.eval()\n",
    "with torch.no_grad():\n",
    "    predicted_probs = squat_model(squat_X_test)\n",
    "\n",
    "threshold = 0.8  # 임계값 설정\n",
    "predicted_labels = (predicted_probs > threshold).numpy().astype(int)\n",
    "\n",
    "# 정확도 계산\n",
    "accuracy = accuracy_score(squat_y_test, predicted_labels)\n",
    "print(f\"정확도: {accuracy:.3f}\")\n",
    "\n",
    "# 정밀도 계산\n",
    "precision = precision_score(squat_y_test, predicted_labels, average='micro')\n",
    "print(f\"정밀도: {precision:.3f}\")\n",
    "\n",
    "# 재현율 계산\n",
    "recall = recall_score(squat_y_test, predicted_labels, average='micro')\n",
    "print(f\"재현율: {recall:.3f}\")\n",
    "\n",
    "# F1 점수 계산\n",
    "f1 = f1_score(squat_y_test, predicted_labels, average='micro')\n",
    "print(f\"F1 점수: {f1:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "e57f97ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x4800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = multilabel_confusion_matrix(squat_y_test, predicted_labels)\n",
    "\n",
    "def plot_multilabel_confusion_matrix(confusion_matrix):\n",
    "    num_classes = len(confusion_matrix)\n",
    "    fig, axes = plt.subplots(nrows=num_classes, ncols=1, figsize=(8, 6 * num_classes))\n",
    "\n",
    "    for i in range(num_classes):\n",
    "        sns.heatmap(confusion_matrix[i], annot=True, cmap=\"Blues\", fmt=\"d\", ax=axes[i])\n",
    "        axes[i].set_title(f\"Class {i+1} Confusion Matrix\")\n",
    "        axes[i].set_xlabel(\"Predicted label\")\n",
    "        axes[i].set_ylabel(\"True label\")\n",
    "        axes[i].set_xticklabels(['True', 'False'])\n",
    "        axes[i].set_yticklabels(['True', 'False'])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 다중 레이블 오차 행렬 시각화\n",
    "plot_multilabel_confusion_matrix(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50889ab8",
   "metadata": {},
   "source": [
    "### Deadlift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "id": "d9c8d471",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 생성\n",
    "input_size = 132\n",
    "output_size = 8\n",
    "deadlift_model = DeepClassifier(input_size, output_size)\n",
    "deadlift_criterion = nn.BCELoss()\n",
    "deadlift_optimizer = optim.Adam(deadlift_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "id": "2a73ae3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OqAD29vYAxMTE4OXlVa7hMJkELYQQolbKnfPj4OBg5paIipT751neOV0SAAkhhKjVZNirdqmoP08JgIQQQghR50gAJIQQQtRyAQEBLFq0yNzNqFYkABJCCCGqCY1GU+Rr1qxZZXru/v37efzxx8vVtv79+/PCCy+U6xnViawCq2rnNkLQbWBpbe6WCCGEqGauX7+uP161ahUzZszg7Nmz+jInJyf9saIoaLVarKyK/1Vev379im1oLSA9QFUoc9Mc+OFebv3+krmbIoQQohry8fHRv1xdXdFoNPrzM2fO4OzszD///EOnTp2wtbVlx44dXLx4kVGjRuHt7Y2TkxNdunRh8+bNRs/NPwSm0Wj44osvGDNmDA4ODgQHB/Pnn3+Wq+2//fYbrVq1wtbWloCAABYuXGh0/dNPPyU4OBg7Ozu8vb2555579Nd+/fVX2rRpg729PR4eHgwaNIiUlJRytac4EgBVodXX1X0o6p1YATsWmbUtQghRFymKQmpmdpW/FEWpsO/w2muvMW/ePE6fPk3btm1JTk7m9ttvJzQ0lMOHDzNs2DBGjhxJWFhYkc956623uO+++zh27Bi33347DzzwAHFxcWVq08GDB7nvvvsYN24cx48fZ9asWUyfPp0VK1YAcODAAaZMmcLs2bM5e/Ys69evp2/fvoDa6zV+/HgeeeQRTp8+zdatW7nrrrsq9GdmigyBVSGn9qOZd/Yor1n/BJtnkmVhR1K7R3B3tDF304QQok5Iy9LScsaGKv/cU7OH4mBTMb9yZ8+ezeDBg/Xn7u7utGvXTn8+Z84cfv/9d/7880+effbZQp8zceJExo8fD8C7777L4sWL2bdvH8OGDSt1mz744AMGDhzI9OnTAWjWrBmnTp1iwYIFTJw4kbCwMBwdHbnjjjtwdnbG39+fDh06AGoAlJ2dzV133YW/vz8Abdq0KXUbSkt6gKpQJ/96LNXeyQdZarefZsM0Xp63iKuxldvNJ4QQovbo3Lmz0XlycjIvvfQSLVq0wM3NDScnJ06fPl1sD1Dbtm31x46Ojri4uOjTTJTW6dOn6dWrl1FZr169OH/+PFqtlsGDB+Pv70+TJk146KGH+P7770lNTQWgXbt2DBw4kDZt2nDvvfeyfPlybt26VaZ2lIb0AFWhBq72PN63CRvPTqBxXAz3WG7jQ4sPWb2jPRNHDTF384QQotazt7bk1OyhZvnciuLo6Gh0/tJLL7Fp0ybef/99mjZtir29Pffccw+ZmZlFPsfa2ngxjkajQafTVVg783J2dubQoUNs3bqVjRs3MmPGDGbNmsX+/ftxc3Nj06ZN7Nq1i40bN7JkyRLeeOMN9u7dS2BgYKW0B6QHqMq9fnsL/nmhL8tdprBf1wwXTSpdDr7E1tMR6HSVO94phBB1nUajwcHGqspflbkb9c6dO5k4cSJjxoyhTZs2+Pj4cOXKlUr7PFNatGjBzp07C7SrWbNm+nxdVlZWDBo0iPnz53Ps2DGuXLnCv//+C6h/Lr169eKtt97i8OHD2NjY8Pvvv1dqm6UHyAw0Gg0L7+/KziMf0XT/fbSyuMqC72axodMUnuzXBH8Px+IfIoQQQgDBwcGsXr2akSNHotFomD59eqX15Ny4cYMjR44YlTVo0IAXX3yRLl26MGfOHMaOHcvu3bv5+OOP+fTTTwFYu3Ytly5dom/fvtSrV49169ah0+lo3rw5e/fuJTQ0lCFDhuDl5cXevXu5ceMGLVq0qJTvkEt6gMykdUNXnhjRk33NXwHgGas/CN13lH4LtnL6eqKZWyeEEKKm+OCDD6hXrx49e/Zk5MiRDB06lI4dO1bKZ/3www906NDB6LV8+XI6duzIzz//zE8//UTr1q2ZMWMGs2fPZuLEiQC4ubmxevVqBgwYQIsWLVi6dCk//vgjrVq1wsXFhW3btnH77bfTrFkz3nzzTRYuXMjw4cMr5Tvk0iiVvc6sBkpMTMTV1ZWEhARcXFwq9bMysrJJWzoIt9jD/JLdl5ezn+Sp/kG8OiykUj9XCCFqu/T0dC5fvkxgYCB2dnbmbo6oIEX9uZbm97f0AJmZrbUVbmPeB+Auy+0EaK7jUIGT5YQQQghRkARA1UGjzmQEDsJSo/CU5V8s/vc82drKGb8VQgghhARA1YbtgFcBtRfIU3uT+7/YS2R8mplbJYQQQtROEgBVF35dSfPtjrVGy92W29h3OY4vd1w2d6uEEEKIWkkCoGrEvutEAB513gsonI9JNmt7hBBCiNpKAqDqpMVIsHakXnoYHTXnORoeL5sjCiGEEJVAAqDqxNYJWt4JwN3WO0lIy2Lxv+crPSOuEEIIUddIAFTdtL4bgNH2RwGFRZvP89Ivx8zbJiGEEKKWkQCougnoA9aOOGbE0EpzBYDfDl0zb5uEEEKIWkYCoOrG2g6CbgNgauML+uL0LK25WiSEEELUOhIAVUfN1fwnAywO4WSr5qsNj0s1Z4uEEEJUAY1GU+Rr1qxZ5Xr2mjVrKqxeTScBUHUUPBQAzfWjtHbLAmDytwdkRZgQQtRy169f178WLVqEi4uLUdlLL71k7ibWGhIAVUdO9aF+CwD62p4D4EpsKtsv3DRnq4QQQlQyHx8f/cvV1RWNRmNU9tNPP9GiRQvs7OwICQnh008/1d+bmZnJs88+S4MGDbCzs8Pf35+5c+cCEBAQAMCYMWPQaDT689LS6XTMnj2bRo0aYWtrS/v27Vm/fn2J2qAoCrNmzaJx48bY2tri6+vLlClTyvaDqgBWZvtkUbSAXnDjNE1SjwHNAfjlQDj9mtU3b7uEEKImUxTIMsOUAmsH0GjK9Yjvv/+eGTNm8PHHH9OhQwcOHz7M5MmTcXR05OGHH2bx4sX8+eef/PzzzzRu3Jjw8HDCw8MB2L9/P15eXnz99dcMGzYMS8uyJd3+6KOPWLhwIcuWLaNDhw589dVX3HnnnZw8eZLg4OAi2/Dbb7/x4Ycf8tNPP9GqVSuioqI4evRouX4m5SEBUHXl3xP2f0FH5SRwLwB/H7/Ou+lZuNhZm7dtQghRU2Wlwru+Vf+5r0eCjWO5HjFz5kwWLlzIXXfdBUBgYCCnTp1i2bJlPPzww4SFhREcHEzv3r3RaDT4+/vr761fX/3Hs5ubGz4+PmVuw/vvv8+rr77KuHHjAHjvvffYsmULixYt4pNPPimyDWFhYfj4+DBo0CCsra1p3LgxXbt2LXNbykuGwKor/14A1E85z8t9vQH1Hy7RCenmbJUQQggzSElJ4eLFizz66KM4OTnpX2+//TYXL14EYOLEiRw5coTmzZszZcoUNm7cWKFtSExMJDIykl69ehmV9+rVi9OnTxfbhnvvvZe0tDSaNGnC5MmT+f3338nOzq7QNpaG9ABVV84+4B6EJu4izzSN5Y+zTpyLTiY6MYNgb2dzt04IIWomawe1N8Ycn1sOyclqbsjly5fTrVs3o2u5w1kdO3bk8uXL/PPPP2zevJn77ruPQYMG8euvv5brs0ujqDb4+flx9uxZNm/ezKZNm3j66adZsGAB//33H9bWVT+yIQFQdda4O8RdhGsH8HYZlBMASQ+QEEKUmUZT7qEoc/D29sbX15dLly7xwAMPFFrPxcWFsWPHMnbsWO655x6GDRtGXFwc7u7uWFtbo9WWfU85FxcXfH192blzJ/369dOX79y502goq6g22NvbM3LkSEaOHMkzzzxDSEgIx48fp2PHjmVuV1lJAFSd+XaAI99D5CG8nO8AIEoCICGEqJPeeustpkyZgqurK8OGDSMjI4MDBw5w69Ytpk6dygcffECDBg3o0KEDFhYW/PLLL/j4+ODm5gaoK8FCQ0Pp1asXtra21KtXr9DPunz5MkeOHDEqCw4O5uWXX2bmzJkEBQXRvn17vv76a44cOcL3338PUGQbVqxYgVarpVu3bjg4OPDdd99hb29vNE+oKkkAVJ017KS+RxykeXf1Xyx7L8fxzG1mbJMQQgizeOyxx3BwcGDBggW8/PLLODo60qZNG1544QUAnJ2dmT9/PufPn8fS0pIuXbqwbt06LCzU6b4LFy5k6tSpLF++nIYNG3LlypVCP2vq1KkFyrZv386UKVNISEjgxRdfJCYmhpYtW/Lnn38SHBxcbBvc3NyYN28eU6dORavV0qZNG/766y88PDwq/GdVEhpFUo0XkJiYiKurKwkJCbi4uJivIdmZMLchaDO5/MBObvvyKrZWFpx8ayhWljJ/XQghipKens7ly5cJDAzEzs7O3M0RFaSoP9fS/P6W36LVmZUNeLcGwD/9LHbWFmRk6zgWkWDmhgkhhBA1mwRA1V3OMJhF5CEcbNQRy7s+3WXOFgkhhBA1ngRA1Z1vB/U98gjdm7jrizOyJTu8EEIIUVYSAFV3Pm3U9+jjLLqvvb748s0U87RHCCGEqAUkAKru6jcHCytIT8AmJZLO/uqyxWPXZB6QEEKUhKz1qV0q6s9TAqDqzsoW6oeox1HH6d5EXS64UzLDCyFEkXJ3F05NNUPyU1Fpcv88y7t7tOwDVBN4t4boExB9go7+6qTo89HJZm6UEEJUb5aWlri5uRETEwOAg4MDmnJmZBfmoygKqampxMTE4ObmVuaM9rkkAKoJfFrDMSDqOF5N1T0PbiZnmLdNQghRA+RmPs8NgkTNV96M9rmqRQD0ySefsGDBAqKiomjXrh1LliwxyiuS1+rVq3n33Xe5cOECWVlZBAcH8+KLL/LQQw/p60ycOJFvvvnG6L6hQ4eyfv36Sv0elSZnLyCiT1Df2RaAmKQMftoXxriujc3YMCGEqN40Gg0NGjTAy8uLrKwsczdHlJO1tXW5e35ymT0AWrVqFVOnTmXp0qV069aNRYsWMXToUM6ePYuXl1eB+u7u7rzxxhuEhIRgY2PD2rVrmTRpEl5eXgwdOlRfb9iwYXz99df6c1tb2yr5PpUidyVY3GXcrQw9P6+tPs5dHRthYyVTuYQQoiiWlpYV9otT1A5m/835wQcfMHnyZCZNmkTLli1ZunQpDg4OfPXVVybr9+/fnzFjxtCiRQuCgoJ4/vnnadu2LTt27DCqZ2tri4+Pj/5VVNK3as/RE5wbAArWN88YXboaK8vhhRBCiNIyawCUmZnJwYMHGTRokL7MwsKCQYMGsXv37mLvVxSF0NBQzp49S9++fY2ubd26FS8vL5o3b85TTz1FbGxsoc/JyMggMTHR6FXt6IfBjnNPp0b6YskOL4QQQpSeWQOgmzdvotVq8fb2Nir39vYmKiqq0PsSEhJwcnLCxsaGESNGsGTJEgYPHqy/PmzYML799ltCQ0N57733+O+//xg+fDharendk+fOnYurq6v+5efnVzFfsCL55ARAUSdYcE9bejf1BOBkZDUM1oQQQohqzuxzgMrC2dmZI0eOkJycTGhoKFOnTqVJkyb0798fgHHjxunrtmnThrZt2xIUFMTWrVsZOHBggedNmzaNqVOn6s8TExOrXxCUZyK0RqPB0kJdyjnvnzNM7tNEfy6EEEKI4pk1APL09MTS0pLo6Gij8ujo6CKXuFlYWNC0aVMA2rdvz+nTp5k7d64+AMqvSZMmeHp6cuHCBZMBkK2tbfWfJO3TVn2PPgU6LV0D3fnv3A0A4lIy9avDhBBCCFE8sw6B2djY0KlTJ0JDQ/VlOp2O0NBQevToUeLn6HQ6MjIK3xfn2rVrxMbG0qBBg3K116w8gsDKHrJSIO4yE3sG6C/JnkBCCCFE6Zh9FdjUqVNZvnw533zzDadPn+app54iJSWFSZMmATBhwgSmTZumrz937lw2bdrEpUuXOH36NAsXLmTlypU8+OCDACQnJ/Pyyy+zZ88erly5QmhoKKNGjaJp06ZGy+RrHAtL8GqhHkcfx9HWiubezoAEQEIIIURpmX0O0NixY7lx4wYzZswgKiqK9u3bs379ev3E6LCwMCwsDHFaSkoKTz/9NNeuXcPe3p6QkBC+++47xo4dC6h7PRw7doxvvvmG+Ph4fH19GTJkCHPmzKn+w1zF8W4FkYfUYbBWY/BwsoFoiE3ONHfLhBBCiBpFo0ia3AISExNxdXUlISEBFxcXczfHYM9nsP41aDESxn7HlB8P8+fRSN64vQWT+zYxd+uEEEIIsyrN72+zD4GJUsgdAos5DYC/hwMAF29IYlQhhBCiNCQAqkm8WqrvcZcgK40WDdTo9vR12QtICCGEKA0JgGoSx/rg4AGKDm6e0wdAR68loNXJSKYQQghRUhIA1SQaDdTPHQY7g7+7g/7S3Z/tMlOjhBBCiJpHAqCaxiNIfY+7iEWe3Z+PhMebpz1CCCFEDSQBUE3joe6ATewFAKbf0dKMjRFCCCFqJgmAapp8AdB9nQ2Z4dMyTSd7FUIIIYQxCYBqGn0AdAkUBSdbK6wt1aGwW6myIaIQQghREhIA1TT1AkBjAZlJkByDRqOhnoMNoCZFFUIIIUTxJACqaaxswK2xepwzDObhpKb4uHYrzVytEkIIIWoUCYBqonzzgLoFugMQejraXC0SQgghahQJgGqifAFQ/+b1Adh/Jc5cLRJCCCFqFAmAaiJ9AHQRgHaN3AC4EptKQmqWmRolhBBC1BwSANVEuZsh5vQA1XO0wc/dHoDjEQnmapUQQghRY0gAVBPl9gDFXQKduvdP25xeoGMR8eZpkxBCCFGDSABUE7k0Aktb0GVBfBgAbRu6AnAsXHqAhBBCiOJIAFQTWViAexP1OE6dB5TbA7T+ZBTnopPM1DAhhBCiZpAAqKbSzwNSA6DWDV30l345EG6OFgkhhBA1hgRANVW+pfDOdtb4utoBkJwhOcGEEEKIokgAVFPlC4AAHu4ZAEBGlgRAQgghRFEkAKqpTARADjaWAKRKVnghhBCiSBIA1VS5AVB8OGSmAGBvYwWoE6H3Xoo1V8uEEEKIak8CoJrKqT44NwAUuH4UMPQAAYz9fI+ZGiaEEEJUfxIA1WQNO6nv1w4AYJ8nABJCCCFE4SQAqskadVbfIw4C4JgzBJYrW6ur6hYJIYQQNYIEQDVZbg9QxCHAeAgMIDkju6pbJIQQQtQIEgDVZN6t1feEMMhMwc7a+I8zIU0ywwshhBCmSABUkzm4g3099TjuEpYWxn+ciWnSAySEEEKYIgFQTaffD+giAR4OTMzZDBHgRKQkRhVCCCFMkQCopnPPzQl2AY1Gw6w7WxHi4wzAwo3nzNgwIYQQovqSAKimy+0BirukLxreugEA1pYac7RICCGEqPYkAKrpPAw9QLnu6thQLUrJRFEUc7RKCCGEqNYkAKrp9AHQRUORkw0Amdk6WQovhBBCmCABUE2XOwco9SakxQPgYGOl3xPoZnKmmRomhBBCVF8SANV0tk7g5KMe5+kF8nSyBSAmMd0crRJCCCGqNQmAaoP6zdT3G2f0RUH1HQE4G51kjhYJIYQQ1ZoEQLVB7o7Q0Sf1Ra0bugJwIkL2AhJCCCHykwCoNvBupb5Hn9AXtfLNDYASzdEiIYQQolqTAKg2yBsA5Sx7b93QBYBz0UlkZGvN1TIhhBCiWpIAqDaoHwIaC0iNheRoABq62eNqb022TuFCTLKZGyiEEEJULxIA1QbW9lAvUD2+qaa/0Gg0+onQl2+mmKtlQgghRLUkAVBtYWJDxEBPJwAu35AASAghhMhLAqDaIndDxDhDANTcRw2AtpyNMUeLhBBCiGpLAqDaQt8DZEiKOrqDmhPsUFg8CalZ5miVEEIIUS1JAFRbuDdR3/P0AHk52+FsZwXAjeQMc7RKCCGEqJYkAKotcnuA4i6DTqcvrp+TEuOmBEBCCCGEngRAtYWrH1jagjYDbl3WF+fmBJMdoYUQQggDCYBqCwtL8GqhHkcd1xd7OtsA8Pbfp9lyRiZDCyGEECABUO3SoK36nicAyh0CA1gUer6qWySEEEJUSxIA1SY+uQHQMX1Rw3r2+mN7a/njFkIIIUACoNolNwC6nicAcnPQHzvYWFV1i4QQQohqSQKg2sS7pfqeHAVptwBwsTcEPbZW8scthBBCgARAtYutMzg3UI9zNkTsEuCuv/zPiSiu3Uo1R8uEEEKIakUCoNrGo6n6HqtOeLaztmTZQ530l9/4/YQ5WiWEEEJUKxIA1Tb1Q9T3PPOAPBxt9MdnohKrukVCCCFEtSMBUG3TqLP6HnFAX9SxcT1GtvMFICk9G0VRzNEyIYQQotqQAKi2aZgTAEUegexMACwsNLx3dxsAUjO1JGdkm6lxQgghRPUgAVBt4xEEdm5qSoxow3wfBxsrnG3VFWHRiZIXTAghRN0mAVBto9EYhsGuHTC65OWi7godGZ9W1a0SQgghqhUJgGqjRl3U94h8AZCzHQATvtrHH0ciqrpVQgghRLUhAVBt1DBn2XvkEaNiT2dDXrCvdlxGCCGEqKskAKqN6jdX3+MugdYw4flWSqb++Oi1BNKztFXdMiGEEKJakACoNnJpBFb2oMuC+Kv64hAfZ6Nqn2+7VNUtE0IIIaqFahEAffLJJwQEBGBnZ0e3bt3Yt29foXVXr15N586dcXNzw9HRkfbt27Ny5UqjOoqiMGPGDBo0aIC9vT2DBg3i/Pnzlf01qg8LC/DM2RH65jl98bMDmtKvWX39+QebzsmeQEIIIeokswdAq1atYurUqcycOZNDhw7Rrl07hg4dSkxMjMn67u7uvPHGG+zevZtjx44xadIkJk2axIYNG/R15s+fz+LFi1m6dCl79+7F0dGRoUOHkp6eXlVfy/w8m6nveQIgNwcbPhrX3qjah5vOIYQQQtQ1Zg+APvjgAyZPnsykSZNo2bIlS5cuxcHBga+++spk/f79+zNmzBhatGhBUFAQzz//PG3btmXHjh2A2vuzaNEi3nzzTUaNGkXbtm359ttviYyMZM2aNVX4zczMRAAEahA0ok0D/fnify9UZauEEEKIasGsAVBmZiYHDx5k0KBB+jILCwsGDRrE7t27i71fURRCQ0M5e/Ysffv2BeDy5ctERUUZPdPV1ZVu3boV+syMjAwSExONXjWeZ7D6fqNgD89DPfz1x3bWZo+BhRBCiCpn1t9+N2/eRKvV4u3tbVTu7e1NVFRUofclJCTg5OSEjY0NI0aMYMmSJQwePBhAf19pnjl37lxcXV31Lz8/v/J8reohbw9Qvnk+XQLcGRjiBUB6lk72BBJCCFHn1Mh//js7O3PkyBH279/PO++8w9SpU9m6dWuZnzdt2jQSEhL0r/Dw8IprrLl4NAU0kB4PKTeMLllaaPji4c768+d/OkJ4XGrVtk8IIYQwIytzfrinpyeWlpZER0cblUdHR+Pj41PofRYWFjRtqq5yat++PadPn2bu3Ln0799ff190dDQNGhjmukRHR9O+fXuTz7O1tcXW1tbktRrL2h7qBcCtyxBzCpy8jC5rNBqj892XYvFzd6jCBgohhBDmY9YeIBsbGzp16kRoaKi+TKfTERoaSo8ePUr8HJ1OR0aGmuAzMDAQHx8fo2cmJiayd+/eUj2zVvBRM8ATddzk5SEtDcOER8Pjq6BBQgghRPVg9iGwqVOnsnz5cr755htOnz7NU089RUpKCpMmTQJgwoQJTJs2TV9/7ty5bNq0iUuXLnH69GkWLlzIypUrefDBBwG1Z+OFF17g7bff5s8//+T48eNMmDABX19fRo8ebY6vaD4+bdX3QgKgJfd34Im+TQA1Q/xDX+7l43/r0H5JQggh6iyzDoEBjB07lhs3bjBjxgyioqJo374969ev109iDgsLw8LCEKelpKTw9NNPc+3aNezt7QkJCeG7775j7Nix+jqvvPIKKSkpPP7448THx9O7d2/Wr1+PnZ1dlX8/s2pQdABka2VJUy8nADafVocht5+/ybMDgqukeUIIIYS5aBTZCriAxMREXF1dSUhIwMXFxdzNKbvESPigBWgsYVo42DgWqPLP8es89f0ho7Ir80ZUVQuFEEKIClOa399mHwITlci5Abg1BkULV3aYrOJoW7ATUKeTmFgIIUTtJgFQbabRQNOcDSEvbDZZxdHWskBZqmSJF0IIUctJAFTbFRsAFewBSs3IrswWCSGEEGYnAVBt599LfY+7BOkJBS472hQMgJIlABJCCFHLSQBU29m7gbOvenzjbIHLpnqAUjJkCEwIIUTtJgFQXVC/ufoec7rAJVNzgDacLDwPmxBCCFEbSABUF3i1UN9vnClwydbKkgl5ssMDfLzlAicjCw6XCSGEELWFBEB1Qf0Q9d1EAAQwe1RrHGyMe4LGLdvD1diUym6ZEEIIYRYSANUFuT1AMaYDIIC/p/Th6f5B+Lqqu2UnZWTTb8FWjl2Lr4IGCiGEEFVLAqC6IHcOUFIkpMWbrBLo6cgrw0J4pHegUfmdH+9ENgsXQghR20gAVBfYueZZCVZ4LxDAA938ebp/kFHZrdSsymqZEEIIYRYSANUVDdqp7+F7i6xmb2PJK8NCjMquyFwgIYQQtYwEQHVFk37q+7kNpb71rk938e66gkvohRBCiJpKAqC6IuQONSv81Z2FJkbN66Nx7Y3OP992iR/2hpGt1VVSA4UQQoiqIwFQXeHmBx0fUo83zSi2+qj2DXk43/5Ar/9+nC7vbJZJ0UIIIWo8CYDqktveBEsbiDgI0aeKre7uaFug7FZqFglpMilaCCFEzSYBUF3iVB+CBqjHZ9cVW72eo7XJ8gsxycxdd5qj4fEV2DghhBCi6kgAVNcEDVTfw/YUW9XNwcZk+T1Ld7Ns2yVGfbKzIlsmhBBCVBkJgOqaRp3U94iDUMxcHmsLTRU0SAghhKh6EgDVNd5t1HlAaXEQd6nIqgNaeDG4pXeRdWRCtBBCiJpIAqC6xsoGfNqqx9cOFFnV1sqS5RM64+/hUGidxLTsimydEEIIUSUkAKqL/Huq72fWlqh6UZ08vx26xunriRXQKCGEEKLqSABUF7Udq76f/QdS44qtfkfbBoVem732FMM/2i5DYUIIIWoUCYDqIp/W4NMGdFnw94ugLXoY6/lBwXw0rj2zR7UqtE5MUkZFt1IIIYSoNBIA1VWDZ4PGAk6uhr2fFVnV1sqSUe0b0rFxvULrjPp4J38ciajoVgohhBCVQgKguipoANy+QD3e/SnotMXe0rqhK4vGtjd5LSoxned/OsL6E9eJT82swIYKIYQQFU8CoLqsw0Ng5wZJkSXaGBFgdIeGRV5/8rtDjFi8Q5KmCiGEqNYkAKrLrGwheLB6fGFTiW/736BmWFsWvkliRHwa8ZIvTAghRDUmAVBd13SQ+n7pvxLf8vygYA5OH1xkHUmYKoQQojqTAKiuC+ijvl8/AukJJb7N2daKMUUMh60+dI0u72zm652Xy9lAIYQQouJJAFTXuTYE9yBQdHB1V4lv02g0fDi2PTPuaGny+idbLnIjKYO3/jolewQJIYSodiQAEhDYV32/sLlcj3GwsTRZfvlmSrmeK4QQQlQ0CYAENB+uvp9eC9rSzd3pE+wJqMHPsZlDTNZ56Mt9vPP3qXI1UQghhKhIEgAJaNIfHOtDchSc+qNUtwZ7O7P+hT5sf+U2rCwtuLtjI/21gJwkqhHxaSzffpmYpHS0OnU4TFEU2S9ICCGE2ZQpAAoPD+fatWv683379vHCCy/w+eefV1jDRBWysoUuk9XjXYuLzn5qQoiPCx5OtgDMHtWKLx/uzNm3h9GqoatRva7vhPLyL0cB+HDzedrP3sS2czfK334hhBCilMoUAN1///1s2bIFgKioKAYPHsy+fft44403mD17doU2UFSRLo+BlR1cPwpXdpT5MY62Vgxs4Y2tlSUejjYFrq8+rKbLWBx6HoAJX+0jNlnyiAkhhKhaZQqATpw4QdeuXQH4+eefad26Nbt27eL7779nxYoVFdk+UVUcPaD9A+rxnk8r5JG5w135tXtro9H5jgs3K+TzhBBCiJIqUwCUlZWFra065LF582buvPNOAEJCQrh+/XrFtU5Ura45w2DnN5VqT6DC3NOpEU62VgXK82+SmJ5VfB4yIYQQoiKVKQBq1aoVS5cuZfv27WzatIlhw4YBEBkZiYeHR4U2UFQhrxZQPwR0WXB2fbkf16FxPY7PGsLO1wbw+u0htG3karJecoYEQEIIIapWmQKg9957j2XLltG/f3/Gjx9Pu3btAPjzzz/1Q2Oihmo5Sn3f+xlos8v9OI1GQ0M3ex7vG8RD3f1N1knNyGbtsUjWnzD0HsrmiUIIISqTRinjbxqtVktiYiL16tXTl125cgUHBwe8vLwqrIHmkJiYiKurKwkJCbi4uJi7OVUrMRI+6Q4ZCTBgOvR9qcIeveviTe5fvrfIOvveGMgLPx0hIj6Nv6f0MTmEJoQQQphSmt/fZeoBSktLIyMjQx/8XL16lUWLFnH27NkaH/zUeS6+MPw99fjft2FvxW1tUM/BsCqsZ5AHj/dtUqDOyCU72HUxlquxqXz87wVe/fUYMYnpFdYGIYQQAsoYAI0aNYpvv/0WgPj4eLp168bChQsZPXo0n332WYU2UJhBu3E5+wIpsP5ViDiklpdzWMrNwVp/PKlXIJ5OBZfJRycalsQv/e8iqw6E89Zfsou0EEKIilWmAOjQoUP06aNmEf/111/x9vbm6tWrfPvttyxevLhCGyjMQKOB2xdA67vVJKl/TYHD38G7DeHE6jI/1s3eEPBYWkDeVfKd/OuZuEN1+noiB6/eIindsHosPUsr+wcJIYQoszIFQKmpqTg7OwOwceNG7rrrLiwsLOjevTtXr16t0AYKM9FoYNg8sHODqOPwxzOQlQL/zinzI+2sDf+5ZWsVohIMQ1svDmlW6H2XbqZw92e7eO234wBkaXXc+fEOesz7V4bHhBBClEmZAqCmTZuyZs0awsPD2bBhA0OGqEkwY2Ji6t6k4drMyQuGvmNcdusKZJYtu7tGo6FnkAfujjb0bOrJ0FY+ADT3dqZ7oIc+sWqvpqa3Uvj7uLpKbMf5m5yLTiYzW8eFmOQytUUIIUTdVqYlNjNmzOD+++/nf//7HwMGDKBHjx6A2hvUoUOHCm2gMLMOD4JLQ0gIh41vqhskRh6GgN5letx3j3YjU6vDztqS7k3cWftcb/w9HLCw0LBiUlci49M4Eh7PzguxJu8f9clOOvi56c+TM8q/VF8IIUTdU+Zl8FFRUVy/fp127dphYaF2JO3btw8XFxdCQkIqtJFVrU4vgy/Kqofg9J8wcCb0mVppH7Pvchz3LdtdorpB9R0Z16Uxk02sKBNCCFG3VPoyeAAfHx86dOhAZGSkPjN8165da3zwI4rgl7PJ5bX9lfox3i62Ja578UYK76w7ze6LsWh1CnsvxZKWKTtLCyGEKFqZAiCdTsfs2bNxdXXF398ff39/3NzcmDNnDjqdrqLbKKoLv27q+9WdkJVWaR/j7WJX6nvWHovk822XGPv5HlrMWC9DY0IIIYpUpjlAb7zxBl9++SXz5s2jV69eAOzYsYNZs2aRnp7OO++8U8wTRI3UsBO4+qnzgfZ/qSZPtSp5b01J2Vlb8nT/IKITM0jP0uonPxdlz6VYbqVG6c9fX32cxeNlPpoQQgjTyjQHyNfXl6VLl+qzwOf6448/ePrpp4mIiKiwBpqDzAEqwsEV8NfzhvMOD8GdS9Rl85Xg98PX+N+qowC8OLgZCzedM1nPykKDo62VUab5DS/0pbmPc6W0SwghRPVT6XOA4uLiTM71CQkJIS4uriyPFDVFx4eh6SDD+eGVcPHfSvu40e0bMv/utmz6X1+eGxhcaL1snWIU/AAMXbSNZ74/xNXYsi3bF0IIUXuVKQBq164dH3/8cYHyjz/+mLZt25a7UaIa02hg3I/qq0VOD+DaFyA5Rl0iX+Efp+G+Ln4Ee6s9OUvGd6CVrwuvDGteovv/Pn6d0Z/sLLLOiYgENp6MKrKOEEKI2qVMQ2D//fcfI0aMoHHjxvo9gHbv3k14eDjr1q3Tp8moqWQIrITSE+DTHpCYM+Rp7QBP7QT3yl+SfiQ8vtjAJq8n+jZhct8meDoVnLMU8NrfAKx5phft8+wxJIQQomap9CGwfv36ce7cOcaMGUN8fDzx8fHcddddnDx5kpUrV5ap0aIGsnOFgTMM51mpcODrKvlov3r2+mMPx4JJVfNbtu0SH/97gU2nolm0+RzZWnW1YpbWsGpx+7kbFd9QIYQQ1VKZVoGBOhE6/2qvo0eP8uWXX/L555+Xu2Gihmg7Vl0Sf34jnF0HR3+EnlPg3D+QmQodHgDbip+I7J4n6Jnctwnz/jlT7D0XYpJZsesKAJHxaTzdvymbT0frr1+7lcbhsFuEno7h2QFNsbO2rPB2CyGEqB7KHAAJAahzgjpPUlNmLO6gLpF/v6nh+oEv4cHfwK1xBX+shi0v9Sc5PZvWDV1wsbPmZGQCd7bz5Z11pwnxcebnA9eM7tHlGe39+cC1AtejEtMZ8+kuAOxtLHnmtqYIIYSoncqcCsOUo0eP0rFjR7Tamr0Tr8wBKqPoU7ByDCTnTCjWWICiU/cPenQzWJR54/Ey2Xo2holfl23X6pHtfFki+wgJIUSNUiWpMIQowLslTP4XhrwNj/0LU46AjTNEHIQza6u8Of2bezGqvW+Z7rWsnG2NhBBCVBOlGgK76667irweHx9fnraI2sC1IfR8znDe/UnYtgB+fgha3QUjPwK7qutVm31na/w9HFkcer5U91lU0saOQgghqodSBUCurq7FXp8wYUK5GiRqme5Pw5Ef1KXyJ1dDZgqM+wEsq2b6mauDNVMHNyMtM5vl2y+brOPjYkdUYrpRmSZPAHQiIgFfN3ujiddCCCFqtlL9Fvr668pZ4vzJJ5+wYMECoqKiaNeuHUuWLKFr164m6y5fvpxvv/2WEydOANCpUyfeffddo/oTJ07km2++Mbpv6NChrF+/vlLaL4rg4K7uDXTgawh9C85vgGV94dGNYOtUZc1o6Wvc6zS0lTcbTqorwLxdCwZAMUnpJKRmcelmMmM+3UVDN3t2vjagytorhBCicpl9DtCqVauYOnUqM2fO5NChQ7Rr146hQ4cSExNjsv7WrVsZP348W7ZsYffu3fj5+TFkyJAC+ceGDRvG9evX9a8ff/yxKr6OMMW+HvSZqvYGAcSchF1LqrQJI9sazwV6rI9hs0Zn24L/Dth+/ib93t/C93vDAIiIT6vcBgohhKhSZg+APvjgAyZPnsykSZNo2bIlS5cuxcHBga+++spk/e+//56nn36a9u3bExISwhdffIFOpyM0NNSonq2tLT4+PvpXvXr1quLriKIMmwv3rlCPd34E149B+H5YGAIr7oCs9CJvLw8rSwsGhnjpz7sEuOuPtTrTCyHjU7P49aDxUvlLN5JZsOEMt1IyK6ehQgghqoRZA6DMzEwOHjzIoEGG5JoWFhYMGjSI3bt3l+gZqampZGVl4e7ublS+detWvLy8aN68OU899RSxsbGFPiMjI4PExESjl6gkLUdD0ADIToPv74Efx0HSdbiyHTZMg79fgpTC/6zKQ5NvYnNjdwcAbm/boET3K4rCnR/v5JMtF3l+1RH+O3eDqIR0ridI75AQQtQ0Zt0I8ebNm2i1Wry9vY3Kvb29OXOm+J19AV599VV8fX2Ngqhhw4Zx1113ERgYyMWLF3n99dcZPnw4u3fvxtKy4O6+c+fO5a233irflxElo9HAPV/Dl4Ph5jnjawdyev2iT8D9q9RUGxXIMl+4v+qJ7uy7HMeINg14f8PZAtnk81t3PIrkjGwAtp27wbac1BlOtlYceHOQ7BwthBA1iNmHwMpj3rx5/PTTT/z+++/Y2dnpy8eNG8edd95JmzZtGD16NGvXrmX//v1s3brV5HOmTZtGQkKC/hUeHl5F36COsneDCX9C50cg5A64byVY5llhFbYbVoyAjOQK/VgvZzuj8wau9oxq3xArSwt+erw7w1v7sOl/fQu9/5kfDpksT87I5kZSRoW2VQghROUyaw+Qp6cnlpaWREdHG5VHR0fj4+NT5L3vv/8+8+bNY/PmzbRt27bIuk2aNMHT05MLFy4wcODAAtdtbW2xtS2YJVxUIpcGcMeHhnPnvyH2AtQLgJ8nQNRx+Ot5GP4e/P4EhIxQA6ZymDq4GVdiU7inU6MC11o0cOGzBzuV+dm3UjPxyxlSi03OwEKjoZ4smxdCiGrLrD1ANjY2dOrUyWgCc+6E5h49ehR63/z585kzZw7r16+nc+fOxX7OtWvXiI2NpUGDks31EGbg1xXa3w/+PWHUp2rZiV9hQRBc2Axr/wc6XdHPKEY9RxtWPtqNUe0bFllv2UOlD4TiciZFp2dp6fT2ZjrM2cRXOy7zyIr9nIxMQFfIRGshhBDmYfYhsKlTp7J8+XK++eYbTp8+zVNPPUVKSgqTJk0CYMKECUybNk1f/7333mP69Ol89dVXBAQEEBUVRVRUFMnJ6nBJcnIyL7/8Mnv27OHKlSuEhoYyatQomjZtytChQ83yHUUpNRsCg+cULP+gBWx4Q50krc2G479CQkTBeuU0tJUPLRuUbrfqW6lqAHTtlmFC9Oy1p/j3TAwjFu9g9tpTFdpGIYQQ5VOhyVDL6uOPP9ZvhNi+fXsWL15Mt27dAOjfvz8BAQGsWLECgICAAK5evVrgGTNnzmTWrFmkpaUxevRoDh8+THx8PL6+vgwZMoQ5c+YUmGxdGEmGWk38MkndPTo/Kztw8ob4q+DgCRP/Bq+QCv3obeduMOGrfSWuP6SlNx5Otvy4L6zQOlfmjaiIpgkhhChEaX5/V4sAqLqRAKiaSIqGn+6HiANF12s1xrC/UAUKj0tl9aEIAus7cjjsFq8ND2Hr2Rs8sfIgAPPvacv28zf562hkiZ737pg23N+tcYHyv49d58DVON4c0RJLC8lBJoQQZVWa399mnQQtRJGcvWFyqLoa7N+3oWFHaH47/DUFzm8Ct8bqkvlzGyHlJjh6VujH+7k78PygYADubKfuJN3A1bCS7L7Ofmh1SokDoNd/P24yAMpdXdbZ350RefYkiklKJ1ur4OtmX+bvIIQQwjQJgET1Z+sEw+cZzu/J2S9IUdS8YlHHYFFbGPoOtL0PbBwrrSltG7kx7642BHiqn9GmYen2KrqVksmN5AyaeTsXuBadJx/ZtVup9H5vCy52Vux7Q/YYEkKIimb2SdBClJlGA8NyAqOsFFj7Anx3D0SfgqSoSvvYcV0b072JBwCtfF14eWjzEt/bYc4mhi3axuGwWyiKQrbWsLJt9eFrbD0bQ2a2jnXHrwOQmJ6tX2EmhBCi4kgAJGq2gF4wYLqacBUgbBd81gM+bA1n/q70j9doNDxzW1P6BJd8+E2nwL1Ld9P57c38c8IQqJ2ISGTi1/tZcziC2GRD0JORXb7l/0IIIQqSAEjUfH1fglevQL/XDGW6LHUC9aK2EF/4yqyKsuyhTjjYlHyYKlunEJuSyXM/Hi5w7ZXfjnEhxrALdnqWtkLaKIQQwkACIFF79HsVHvgVnj8Kfuo2CsRfhaW94bfJEHcJru5W5w5VMAcbK3a8OoDxXRvzeN8m5X5e6JkY/bH0AAkhRMWTZfAmyDL4WiAzFc6th/XTIDnffCC/7tDjaWhxpzqPqJIcDrvF38eu88WOy+V6zvIJnfl29xXuaNuAsV0KriITQgihkn2AykkCoFokMwXOrIMtb8OtK8bXhi+Abo8bl4XthZiT0HEiWJS/g/RWSiYd5mwC4K07WxERn4ajjRUfbj5XpuddmTeCG0kZ7L0cy9BWPljnT3EvhBB1mOwDJEQuG0doey+0HAU3z4K1A/z9IlzaAsd+AksrdU+hns+BnRt8NUS9z8IKOk4o98fb55kX1LFxPR7uGcCpyMQyB0AAd3+2i7C4VOaMasVDPQLK3UYhhKiLJAASdYOVDfi0UY9Hf6bmFYs4qL4AbpwF71aG+kdXVUgAZGtl6KFxsVf/d/N2sdWXPd0/iP7NvQjwdGDgwv9ISs8u8nlanUJYXCoA287fpFsTD+avP8MLg5rRupR7EgkhRF0m/eei7nFpAD2fNS6Luwin/zScX92hTprOSqM8NBoN745pwyvDmuPvoW6e6O5oQ//m9ekZ5MFLQ5rTNdAdL2c7Xr+9hdG9/ZvXL/C8B7/Yqz+2s7ZkyIfb2Hw6hgdyytMytfx8IJybyRnlarcQQtR2MgfIBJkDVAcoClzZoQ51nVwN+z5Xy+9YBGfWwoXNYGkDumwY8ja0G68uq7e0VleaWdkW+fiyWH3oGlN/Pqo/b+LpyKWbKSW+/8q8EUxfc4KVe67StpErfz7bu8LbKIQQ1ZnMARKiOBoNBPZRj71agK0LNOoCzYdBQB91LlBqrHp981uQGgdhu9Xzcxug5Z2w9T11wvSYZWBd/nxdtlaG+UJjO/ux5WxMEbULSkjNYuWeqwAcu5aAoiho8qxy0+kUNBq1VypLq+NwWDzt/FyNPlcIIeoKGQITwt4NBk5Xgx8Az6bw7AEY/5N6rs2AnYsM9Y//DFnpsPVdOPUHHPm+QpphbWkIVl4e1pz41KxS3b8o1Hhi9efbLumP07O0DPzgPx7PyWS/aPM57lu2m7fXni5Hi4UQouaSAEgIUxzcoflw6D1VPdflmZx8bgNc2284D99X4R9vZ21JprbkGyBaWmiIuGU8X2nuP2f0x1vOxHD5ZgqbTkUD8MmWiwD6HiMhhKhrJAASoig9nzM+11iCNhN2LTaUxYdXyEflnYxnZ2XBgnvalvherU7B0bbgiHZapppGI++kaJ1OMVqdJoQQdZH8LShEURzc1Z2jc7W/X30/v9FQlnCtQj4q73IEK0sL7u3sx+LxHegW6F7svc52VkZ7DuWatELtnbqZJ7lqerYWF3tro3qnIhO5/aPtbMlJwbH7YizL/ruIrJEQQtRWEgAJUZy7v1AnRo/+DNqOLXg9MQK0Re/fUxKWFgXTctzZzpdVT/Qo9J41z/QCICk9m7+PXS9wfc+lOLK0Om7k6QG6EJNslLg1LVPLU98f5NT1RCatUIf2xi/fw9x/zvDvmdJNxBZCiJpCAiAhiuPmBxPXqr0/Ab3VpKvebeDeFWDjBIpW3Vn6+3vh6xEQU7aJxX2bedKigQv3dW5U4NqkXgH649xAydJCg2OeQCYhzfSk6Td/P8G644bg6M6Pd3I1NlV/HpeayY0kQ4C0OWeeEEBkQnrpv4gQQtQAsgxeiNLQaOC219UXwOHv4cIm+P4eQ50/p8Bjm0r9aFsrS/55vo/Jay8Oac7XO68AMKq9LxN6BODrZkdmCTLFrzpQ9BylWymZaHWGoa7Hvj1gaJPkGhNC1FLyt5sQ5WFqSOzaPoi9CDpthX2MXZ5Jyw3d7Gnv54aXsx2ONuX/N0xcSia6Qub6JGWUf2hPCCGqIwmAhCiPtvfC2O+hz0swab06Vwhg/WuwIAhmucLvT6oBUTlY5emJ8Xax0x/Xc7Th1WEh5O532NDNsCFjn2DPEj07Ll8PUF6FDasJIURNJ0NgQpRXizvUF0CrMXBlu/EqsaM/wrGf4c7F0OHBcn9c54B6RudP9Q/iqf5B+vPv914lNUNLfFom28/fLPZ5ag+Q6WuJEgAJIWop6QESoiJ1mgidJoFrYwjsC0EDwdFLnSi97mVIiS3zo/94phdfTOhMiE/R+W0e6ObP5L5NsNQUXFWWy8/dHhc79d8/R6/FF1pvw8ko0rPUobwloeeZ8NW+Es07EkKI6k56gISoSBaWMHKRcZlOB8v7w/WjsHcpDHijTI9u5+dWqvrZhXTrBHg4sPXl2/hw0zk+Cj3PH0ciC33G9YR0Rn+yk7+n9GHhJjXVxh9HIvhubxid/esx/Y6WpWqTEEJUF9IDJERls7AwpNTY8xmE7zfsG5QYCaFz4NqBwu8vo4d6+OPjYseo9r4MaemtL+/kr26s6O5oU6LnnIlKYs3hCP356kMRHA2P58sdlzl+LYHe7/3L+hMF9yASQojqTKPIVq8FJCYm4urqSkJCAi4uRQ83CFEiOi180g1iz6vnTj4w7F3Y8SFEHYd6gTDlMBQxbFUWeTPCHw2PZ/Wha0wd3BxXB2v+PBrJlB8Pl/qZbRq6cjwiAYCOjd04FBYPwJV5Iyqs3UIIURal+f0tPUBCVAULS7jvW/DvBWggOQp+fUQNfgBuXYbL/1X4x2ryBFTt/Nx4a1RrXB3UNBg2lmULtuLTDGk18g6zhcWmMunrffxxJMLUbey/EseJnMBJCCHMTXqATJAeIFGpsjPVjRPzBzxNB8ODv1ZZMw6H3WLMp7sq7Hn1nW31O0oPauGFk60VH45tj0aj4WZyBp3f3gzA5bm3GwVmQghRUaQHSIjqzMoGRn6kDnt5tYKH1qjlFzbBxulw4yyc+gMu/qtOoK4kHRrXY/aoVnz7SFfeHdOmwPUn+jUp1fOM0mmcjmHNkUhSc7LRh8cZUm8ky+aKQohqQFaBCWEO7vnm/LS+B078CrsWq69c7R+E0Z9UWjMm9AjQH++5FMufRw0rwqYNb0GwlzMv/XK0zM+/+7NdLJ/Q2SjoiU/NwtlOHYZTFIXfDkUQ4uNM64auZf4cIYQoLekBEsJc8g4D3bUc7v0GnLyN6xz5Dj7tCZ/fBns/h79egDXPVErP0OLxHbDJl/srb7LVsjgTlcSDX+7lZp5s9PGphs0Vt5+/yUu/HOWOJTvK9TlCCFFa0gMkRHVgYQGtRkOzYRB3Ceo3hw2vq/sGxZxU60QeMtRvOhA8g9WJ1DZOMPRd8O9R7mZkao0DK0dbw18Ro9v7sqaIPYMKczU21Wh47FaqYRL1majEMrRSCCHKT3qAhKhOrO3Au6W6amzQW9DufmjSv2C9nR/B0t5w85waGP38kDq5uoI45wQ+jraGHqDbQrzK9CyNBmKTDW2LT8siIj6Nk5EJaCjZZOizUUl8vu2i7EIthKgw0gMkRHVlbQdjPlOPtdnw++Nw4jf1/PoR47opN9RVZcGDy/WRrw0PYcGGsyx9qFNOiSFA6desfpmeqSgQnZiuP49PzaTXvH8BeKx3oL48S6vD2tKCjGwtl2+m0NzbWb9abOiibQBodRjlPRNCiLKSHiAhagJLK7jnK3gj2rj8yR3Q9XH1+N+31V6gs/9ARnKZPubJfkGcfGsovZqqmeRbNHCmvrMt3QLdcXOw4ciMwdhZF/xro0cTjyKfG34rTX98PtrQtvMxhuOUjGx0OoXpa04wbNF23lhzosBzDl69VervJIQQpkgAJERNYm0HfV8GC2u452vwaQN9XgJbV7VX6O368OM4+HKIuoy+DOysDcNeDjZWbH/lNn6c3B0ANwcbDk0v2Mv0/KBgRrRpUOgz8wYuey4ZEsLGphjmBt1IymDEkh38fOAaAD/sDSvwnOycyd87zt/kZKRsqiiEKDsJgISoaW57A6aFQ+u71HNnbxg8y7hOzElYOUbdT6ic7KwtsbAwDIU52BiPnL9/bzu6N/Hgkwc6luh5eXt9rt407A805tNdnL5uPCn6td+OGQU6Wp1CWGwqD365lxGLZeWYEKLsZA6QEDWNRgPW9sZlnSapw1/Xj6oryC5shivbYdMMaDZc3XyxAm38X1/+PRPDpF4B2FoZeoz6NavPtvM3mHlHS349dI3oxAyjFWD5JeXZH8jUBok/7Q9n8+kY/XmWVsfl2BT9uaIopGRqycrWUa+EyV2FEAIkFYZJkgpD1HiZKfBRe0iJAe82EHI79HwObJ0r9WMzsrUkp2fj4WQLwKSv97Hl7I1K+7xzbw+nw+yNpGRqOT5riH6DRSFE3SSpMISo62wc1RVkNk4QfRz+ew9+mQjh+9Ss9Ns/gNNr4fymCv1YWytLffBTUawsCl8qn5apJSUn3caZqKRSPVdRFLQ6+fefEHWVDIEJUVs1HQRPbodvR0P8VXVY7IKakJTQt3IqaWDSOrB3BycvcHCv0CaUNLzo2NiNQ2HxJq819nDg0o0Uk9fyZqZPzsjWL6UvifuX7yUmKZ31L/Qt8T1CiNpD/q8XojZzbwLPH4UWIwupoMDXw+HTbrCkI6TGma52IRT2fwnaLNPXS+jriV347tFuvDY8RF9Wz8GaoPpOhd5TWPADcD3BsL/QpK/3M3bZbrK0xW+WqNUp7L4Uy8UbKQUmXgsh6gYJgISo7TQaGDwbXP0ADQycAQOmQ/9p5N3okLRbEHFQPU6MhKu71V0MT/0J390Ff09VJ1WXQtdA4x4lH1c7egd78mQ/w2aGmdk66juXbdhs3Od7jM4PhcXz7e6rxd6XnqUt0+cJIWoPGQIToi5wbwLPHYL0BHDKs6NzfBgc+d5wHn0SPJvBsr6QHg99XzFeSn/mbxg2F26cA3s3ddisCI/1bkJ0Qjrf5AQlefcYypWRrcPbxa4cX87Yz/vDebR3IFdupvD38etM6OFfYHJ0aqYhAJJpQELUTdIDJERdYWVjHPwADH8PejwLXq3U83/fhpWj1eAHYNt8uHnWUD/+KpzbCJ92hy8HQ3bhS9wBbKwseG14C/25qUWn7o429An2LMMXMu1sdBJbz8Zwx5IdLNhwljazNvL1zstGdfL2AElvkBB1kwRAQtRlts4w9B2YuFYdItNlqdnoLazAJs+S+S6ToV5O3q6f7gdFC7euGPceFcLexpKR7Xzp1dSDAA9HffnXk7rQxNORTx/oSJP6TvhUYC/QxK/3G+0r9NZfp4wCnbw9QGmZEgAJURdJACSEUFd/jfseHDzAIxge/w8e+h2sHcGxPvR5EXw7qHV1eSZCb32vRHnHlozvwPePdTfaUfq25l78+1J/Ogeo84Q2vNDX6J4F97Qt//fKIzTPhoppeYKhNOkBEqJOkjlAQghVg3bwv1NgYQmWOXNmXjwNOq0aIPl1g5Or1fIWI+H6MXVI7PcnICEc6gWoWesHzgCvkEI/pjCuDoZ5OnbWFvQJNgzXdWjsxvX4dKLyZJUvrXUnrpOSmU0DVzss8wRiqYX0AN1IyiA6MZ3WDV3L/JlCiOpLAiAhhIF1vmEouzy//DtPgriLoMuGoXPhwFewYRqcWatev35Ufb+wGZ47CG5+ZW6GBo1R1vm37mxFUH0nvth+mdYNXXj0mwOlfubfx67z97HrAHw1sbO+vLAeoD7z/yU9S8eWl/oT6Olosk6umMR06jvbotEUvmmjEKJ6kSEwIUTJWNnC7Qvgjg/VQKmwvYW0GXBufbk+SqMxXjFmbWmBo60Vzw8KZmALbx7s3rjAPW0bubL+hT5GgVNhEtIMw3hpmQVzkMUkppOepe4nVNw+QT/vD6fru6F8/O+FYj9XCFF9SAAkhCgbNz9waageO3lDzynqC+DSVjUfWWEbK+YK2wPRpwoUawBbK8NfT/mXyTvZFsz59eezvQnxcWFIS59imx4ZbxhKMzUEdiQ8Xn9c3C7Rr/x2DICFm84V+7lCiOpDAiAhRNk9sh46PwIPrYEhc6DlaLX8zFp41xcWBKkbKpoSfQq+GgpfDimww7SFRoNGo+GPZ3rx8xM9cM+X6d3G0jDU1KGxG18+bBjScrYrOLI//27jCdXXbqXqj7/acZmYJDUgytLquOezXTy+8qD+eqqJHqLyCI9L5ZcD4ehkAyIhzErmAAkhys6tsToklsu3Pbg0gsRr6rmig2M/gX+Pgvce/UF9z0xSd6Bu3B1HG0tSMrV08K8HQDs/N5Mfa5WnV+b3p3sZXXOyNfy19s6Y1sQmZ3Jne1+uxaexOPQ8AD/uC9fXSUzPpus7oex7fSDXE9I5cPWW0fM+23qRUe0bFvVTKJX7lu3mekI6ienZPNo7sMKeK4QoHekBEkJUHAtLmPCHcdm5jWpKjfyu7jIcX9wCwB/P9uKRXoEsvLddkR8ztJU6zNWonn2Bay72huGx0e0bMmVgMHbWlkwd3IyWDVwKfeaHm89zI6ngxo5nopJYuPEsuy/G6svWHI5g86loo3o2+YbKsrU6k708ufnLfjkQXuCaEKLqSAAkhKhYnk3hkY3Q5Db1PCkSruVbtZUUZVg1BuqcIaCplzMzRrYsNjdYcx9nQl/sx7rn+xS4Nqy1D72aevDh2HY42hp3crfyNQ6APJ0MQ2s/7gvjsW9Nry5b8u8Fxi/fQ0xSOvuvxPHCqiM89u0Bo8SrmVodH+TMA9LqFG5fvJ07P9lR6FBXRHxakd9RCFG5JAASQlS8xt1gwhpoO1Y9/3USbHkXUmLVZfLf3a0up3f2Va9f2w/ppcvKHlTfCRdNBiTHFCj//rHujOnQqMA947oaL80vbnl7fl3fCeX+5YYErFEJxvsSLQ49z+fbLhKVmM656GRORCQSmWA60MnIKj5rvRCi8kgAJISoPINmqSvEEsLhv/dgQRM1+Ik+AVZ2MPY7NVGrooVD38LOxYaVY/Hh8P196kqxwvxwH3zUXk3qWgKtfI03NXSwMT0N8t5OjajnUHClGUCW1tCjc+lmSoHr7647wx2Ltxvq3DDUyZsLLe8qNyFE1ZP/A4UQlcfFFyZvgdveBNc8e/c07AxP7YJGnSBooFq28Q3YNB3mB8KmmeoO0+c3qCvF0uLhpwcgdI5hPlHCNbi6E7JSjDPWFyF/Nvq+zeqbrNe6oWuJeocuxphOA3Ir1bCq7dINQ528S+5tS7BfUa7MbB2PrtjPsv8ulvgeIUTRJAASQlQu14bQ72WYHAqtxkC3J+Hhv8AjSL0ePLjgPTsXqcFNrsMr1aX1299Xh9AALv5ruB59ssTNmX5HS/1xCx9npgxoWqCOo60Vfu4OxT7rfCEBUF55g6HE9LzL/dWl/IqiGM0lMmXtsUhCz8Qw958zxX6eEKJkJAASQlQNJy+4dwUMfw9s8gQXAb2Lv3ffcsPx2XXq+4VQQ1nUiRI349HegfzwWDdmjmxJjyAP7E0MgznZWvHGiBbFPuvHfcUPvSWlZ/PTvjCuxqaQmGbYUygjW4tOp/DJlgs0f/MfDoXdKvQZ8alZhV4TQpSNBEBCCPOycVSHyIIGwCuXYcQHBevEXzUcn9sAmalwfpOh7MYZyM4s8Uf2bOrJpF6BaDQaHujemG6B7tzTyTBp2tnOCi9nO+7uWHAiNcD/BjUr8Wd9tfMyr60+zrM/HOZKrGE+UFJ6Nk1eX8f7G8+hU2DeusJ7d7J1MmFaiIomAZAQwvz6vQwP/a5mnW9xJ1gXMv9GYwGJEepQWFaKOq/IsT7osuDjzupy+1tXYfNbaq9Q3GX4rDdsW1DoR7vYWbPqiR482S9IX5a7fN7R1jBn6LXhhgz3wd5OvHd3m1J9xeMRCTyRZ4fp/LJygpybyRn8fCCctDzzhfJOvBZCVIxqEQB98sknBAQEYGdnR7du3di3b1+hdZcvX06fPn2oV68e9erVY9CgQQXqK4rCjBkzaNCgAfb29gwaNIjz589X9tcQQlQEp/pw93J1rtBjoWCZsydQy9EQPFQ93r5QfW9zt5qKA9Reou/vgW9HwY4P4Ovb4YexEH0c/n0b4i4V+bF5gx2nnOO8q8S6BrrrjzXA2C6N6d+8PhYa6NHEo1xfGeDKzRQUReHp7w/xyq/HeG+9oUcoO08ApJUUGkJUCLMHQKtWrWLq1KnMnDmTQ4cO0a5dO4YOHUpMTIzJ+lu3bmX8+PFs2bKF3bt34+fnx5AhQ4iIiNDXmT9/PosXL2bp0qXs3bsXR0dHhg4dSnp6uslnCiGqmZAR6lyhRp3hyR3w4Gq4azm0vc9Qx9oROj8KvZ6HwL5qWdotuHVZPc5IgJtnDfV3Li7yI/NummhjqQZA3i6GDRl9XOxo6KbuPN0xJ1XH5w91Zs/rA/nx8e5l/aZ6t1KzOBIez77L6jYAP+SZX5R3CCwzW4bDhKgIGkUxtUd91enWrRtdunTh448/BkCn0+Hn58dzzz3Ha6+9Vuz9Wq2WevXq8fHHHzNhwgQURcHX15cXX3yRl156CYCEhAS8vb1ZsWIF48aNK/aZiYmJuLq6kpCQgItL4VvnCyGqmKLAriUQvhe6P2U8gTp0jjo0BmpW+mOrIDlPugqPpvBc4UNQWp1C0OvqBOvTs4dhb2PJ7ouxjM/Z+PDojCFYWEBCWhaN6hVcIRbw2t+FPtvWyoKMnMClV1MPdl6ILbRuXksf7EhiWjYz/zxJWpY6JHZkxmDcHGyKuVOIuqk0v7/Nmgw1MzOTgwcPMm3aNH2ZhYUFgwYNYvfuQjJI55OamkpWVhbu7mr39OXLl4mKimLQoEH6Oq6urnTr1o3du3ebDIAyMjLIyDDkAEpMLN2OtEKIKqLRQK8ppq8NeBOaDQNrO/BpA+3Gq0FQy1Gw/DaIvaDOEWrU2eTtlhYa9r0+EK2iYG+j9gC1baRunGihASc7KywtNDjbmd4gMT8vZ1ticnKLje3ix3MDgvlm1xUm9Qqg09ubS/SMJ787VKAstwcoM1tHTFK6yWBMCFE8sw6B3bx5E61Wi7e3t1G5t7c3UVFRJXrGq6++iq+vrz7gyb2vNM+cO3curq6u+pefn5/JekKIakyjAb8uavAD4N0SBr8FDTtCoy5q2RcD4Y9n1VVkJni52NHA1V7NTp8UhaOtFbunDWDXawOxtNCUqjl9gg2bLLrZW1Pf2ZaXhjbHw6noPGfFiUpMJyI+jfc3nqX3e1v4+9j1IusfuBLH3HWnSc/SFllPiLrGrD1A5TVv3jx++ukntm7dip2dXZmfM23aNKZOnao/T0xMlCBIiNrk9vdh5RhIi1M3VXRvom6kmHJD3ak6775EN8/D8oFg6wLPH6GBq3vhz82lzaap5hoXlIaAhnFd/JjQI4DfDl0DKJCUNS9nWyuc7Kz0WeKLc+fHO43On/nhEANbDCuwy3Wue5aqvemuDtZM7BlQaPoPIeoas/YAeXp6YmlpSXR0tFF5dHQ0Pj4+Rd77/vvvM2/ePDZu3Ejbtm315bn3leaZtra2uLi4GL2EELWIb3t4+SIE9lPPQ9+CK9vV/YNyd5TO7RUK3wso6iTqE7+V7Pl7PmWz7StMtNxA32b1mXd3W5r7OOvziQV7O5m87eWhzTkycwidA0oQZBUhZPr6YtNkLA49T8sZG/ipBJs3ClEXmDUAsrGxoVOnToSGGnZ01el0hIaG0qNHj0Lvmz9/PnPmzGH9+vV07mw8nh8YGIiPj4/RMxMTE9m7d2+RzxRC1HIWFjDuB7X3J68dH8KyvvBuA/h5Alw/ariWN91GUTZNB2CW9bfMvUsdgrO00LDtldtY+WhX+jfzMqr+/MBgWjRwYUIPfywtNFTEWhRTaTLy5iFLz8k+/9rq4+X+LCFqA7P3hU6dOpWHH36Yzp0707VrVxYtWkRKSgqTJk0CYMKECTRs2JC5c+cC8N577zFjxgx++OEHAgIC9PN6nJyccHJyQqPR8MILL/D2228THBxMYGAg06dPx9fXl9GjR5vrawohqgNbJ3VvoQ9aQnaaWhZxwHA9f1LV3GAoPQGijkPjnmogBZAUDcd+AmdfdYNGRQ0wGv4wQM115uiJs5210VygXP8b3Iz/DTbsJj26fUPWFjOXpyTSs7RMW32ctEwtk3oFMPbzPeV+JsCNpAzWn4xiTIeGOBUxnCdETWL2/5LHjh3LjRs3mDFjBlFRUbRv357169frJzGHhYVhYWHoqPrss8/IzMzknnvuMXrOzJkzmTVrFgCvvPIKKSkpPP7448THx9O7d2/Wr19frnlCQohawsEdHt2oDn9tmglJkWBpA10fh90fG9dNjFCDn79egJOrocVIGPM5HPoW1r9q+vkxp+DM39Dp4RI3aWALL357qidz153mwNXCc4IVJ2T6ev3x+pOmF33YWJa+4//hr/Zx6noi09ecYM+0gfi4yt+louYz+z5A1ZHsAyREHRFzGg6uUAObhp1hfiBk5cwFsndXJ00/8Ku6w3SuPL09heoyGUa8X+rmxKVk8suB8ErN+u7tYsve19VVs6evJ5KamU2bhm5YW2rQaIxXup2ISODijWSe/+mIvmxYKx+WPtSp0tonRHmU5ve32XeCFkIIs/Fqoe44HdBb3T/ottfV8p5ToEnOhOm8wY+ljXHw82ye4TN7d7V3CGD/cogPh73LYPkAiC/ZxGN3Rxue6BdEOz83AFo2qPh/gNXL2UQxW6tj+Efbufuz3bSauZ7HTeQpu2PJDqPgB+BafCqpmYas9kfD44lPLXkiWiGqC+kBMkF6gISooxQFUmPB0RMOfwd/PGO4du8KCNsLez9Tzx//T11ddmkr/DYZhs0F/57wQQv1umdzQyqOAW9C35eNPystHjbPhNiLcPsCNRjLkZyRzdmoJDo2diNw2jp9uUYDL1quIkATzWtZj5FM8Zsgvn57CO/myTQf4uOMooC/hwMbTxmvlr0893YS07P540gEtzX3os/8LSafaWdtwb2d/LiZnME/J6LwdbVj17SBxbYll6IoJGeoQdRfR68zpJU3nuXcH0kIqEE7QQshRLWi0ajBD0C7+8HKDlJuqrnGvFtC08HqNQcPaNBOPW7SH17Om2xZAyjGeciu5ekpyrXxTXVPIoD9X8CIhfpLTrZWdMrJN/bzEz2Ys/YUb41qhW/SKXx+USdqO5HGxKxXODJjCA9/vZ+j4fEmv1JzH+NfAmeikgA4G51UoG5YXCqPf3uQs9FJtPOLKHA9V3qWjpV7rurPI03sYZSQlkVGlhYvl4Lzhd766xTf7r6Cv4cjl2+m8NP+MH6Y3F0mWIsqJf+1CSGEKRYW0MZ4sQW2TjB8XtH3vXgWFjYzLgvfB9kZoNOqmy4m34BjPxuuRx4u9HFdA93567mcnGf/btWX97c8SqPsG7g52HApJtnkvT2DPPBwLHnesH4LDM8vLKAqjFan6HfLVhSFLm9vJlOr48RbQwsENit2XQHg8s0UAI5dS6D1zA388Fg3ejb1LNXnClFWMgdICCEqkrM3+HUzLkuLg7e94JNucOBrWNQGtBlgr/byEHUcskswjybSODfYjHZq4HN/t8b6sn7N6rPt5duYfkdLPrivPe6lCIDK42ayIZ/ijaQMMrXqXKkTEQklTsPx8q/HKqVtQpgiAZAQQlS03v8DNOpQmVdLQ3lCGKx9Qd2DyMZJ3S/Ivh5oM+Hwt3DgK8hKM/3M9AS4oqbB0AX0AWCw7UkAnh8UzEfj2nNmzjC+eaQrjT0ceLR3ID6udlUWAEXlGQY7F23okRr3+R5Cpq8nPM50/rW8IuIN3z02OYO+87ewcOPZIu4QouwkABJCiIrWfDi8cBye2gW9njdd5/ljauJW3w7q+d8vwtr/waYZkJWu5iTL6+8X1cCpXgAW/V8DQHN+IwAONlaMat/QZD6wwnKE5R8ae7iHvz51R1lcumkIei7HphS43mf+FmKSSpbvDNRhsrC4VJb8e6HMbRKiKDIHSAghKoNbTkLltmPVxKoNO6pL4xPC1CEyRw/1etAA45Qb+5ZD9Em4uhPu/Bg6PgQ3L8DxXwANjPjAMAE7NRbSE8Gu9KtV970xiLNRSczfcIaXhjSndUNXnurflJvJGTz342H9/JyS+t+qo4THpfHt7qtGw2F5DXj/P/59sV+JnpeZXcxeS+WQrdVhVYYNIUXtIv8FCCFEZdJoIOR2cPYBvy7Q+m5wbWS43vpusHXNc4OiBj+gJm0FiD6hvvt2gKYDwdYZ7NzUsoRrxTbhlyd7MLFngP582UOdsLTQ0NLXhRWTutK6ofr5Pq52tG7oahSkNPVywtHGEvtCepLy+mDTuUKDH1CX968+XPjqMiN59mRUFIUf94Wx/oTp3a1L4+cD4bSauYH/zt0o97NEzSYBkBBCmJOLLzy1Ex78DW57Qy2zcVbfU27AuQ1wYZN67hlsuC+3hykhvNiP6BLgzlP9g/TnIT7ORdbXaDQ81N0fWysLlj7YieOzhnJ05pASf6WizCtml+usnMnTFnl2pT4Xncy01cd58ruD6HSl27ouPjWTjSej9M995ddjZGTreOb7Q8XcKWo7CYCEEMLc3Pyg6SDo9wo8fxSmhUOD9uq1H+5TN2UEcA/Kc4+/+p53rpA2C1JiTX5E7hJ1UOcMFWf2qFYcnTmEpl5OWFhosLGqml8XrWdu4Msdl8nWGobA8k6gjk/LKnDPiYgEes37l9WHCvaGjV++l8dXHuSzrReNyitziE3UDBIACSFEdVIvQB02a/9AwWv+PQzHDXPycYXnZHyPOQ0ftYP3g+HcRkO9Kzsg7hKu9tbYkMXH1ovx2Ptesc3QaDSFTqDO5WhjyTePdC32WaWRka1jztpTJKUb0m1E55k8fSMpg/wJDP636ggR8WlM/flogeedvp4IwO+HI4yCHl2+Z+TuTC3qDpkELYQQ1VGXx9TErGG7IeKQOvzl39tw3b+X+n76L9jxIWyeZbi2abq6Z9Cl/yBsFwDWwUM5MmYEDv/sgR17oPuT4ORVriZm6RTcHSpnmf1P+w1DeyciEvXHQxdt4/5ujXl3TBt92bVbBbcO+OVAOOfzbBCZrdMxdNE2/XneAGjpfxeZ988Zvny4MwNbeFfYdxDVm+QCM0FygQkhqh1FUXuGcum0at6x5OjC7ynKmGXQblypbgmZ/g/+2Vd42/orPskexTalA9teuY3e75nOGVaZFo/vwJ3tfAEIeO1vffnglt7MHtWKHnP/LexWvY6N3UjOyNbvW1TPwZrDMypmrpMwD8kGL4QQtU3e4AfAwlJdEm+ZJ4nobW9A+wdL9rzwfaX7/GsHOO75JhtsX6OLxTnmWy9Hp1Bgo0VPp8J7hO7v1hg764r5tfPnEXU1Wd65QgCbTkWXKPgBOBQWb7Rp463ULKNJ1oqiEJ2YLvOFaikJgIQQoqZqcQe8GQ0T/oCn96qTqHs9b0ixkavXCwXvvba/dJ+1/jWsbxk2JfTSxANKgeXxmvyBWh73dfZjxh2tSvyR7o42BVasDQhRh+1SM7XcSMqg8zubS/y8kvjrWCQAF2KSCJy2jm7vhvLkdwdJMDH5WtRsEgAJIURNptGoGem9QtTz+s1g6ml45TL0eBaGvAN9XgSXhmp2+/Gr1HrRJ9WJ0zfOFf7suMvqJo0ZSSYTtjbRXEej0fDMbUEmbjbm42JHez83RnfwJcTHmdua1y/2HkVRcLYzTFUd37Ux47qoy//TsrQs336J+NSyBSad/OuZLN91QV1Fl3dC9b9nYmj31kZe/fUYs/48SWJ60Z+pLeVS/ZJSFIWFG8+y6VQZhz2FEZkELYQQtY21vfoa+o6h7Iltap4xNz9w9oWkSPi0uzqE1vsFNSFrv1cMqTlSYmFxe/V40Fugy1ZXqD1/lN3Te9DD8hQ9LdRcZC8PDeGTLeoy88KGi5p6OQHqEvz1L/QlW6uj6Rv/FPk1FDDKJO9sZ4W9jdrjlJapJSax5Kk18rMpZCdoH1c7AOJSCianXXVAnZit1SnMGd3a5P1xKZkM+XAbg1p4Me/utmVunymbTkXrU4NcmTeiQp9dF0kPkBBC1AWOnobNE4NuM5RrM+C/9+DsOlg1QZ1sDXA+z1L6be+r74F9AUhu2BOA8Z6XCnxM3szvvZu4oYYxMGNkS6N6VpYWzMpXlp+ioM8qDzCohTcOuQFQlpZdF03veVQSVpamh+py+26ytIXP+zkZmcDeS7FsPRtDRHya0RL6n/aHcTM5w2gVW0WJTip8l+280jK1xVcS0gMkhBB1TqdJcOxn0OUbykkIg6M/qivM/nzWUJ6ZpL43HQRA/2H3woovaJlxVK1rYZgH5GGjhexYEnHgs8Q3cA50QffYFiwsC+4pNLFXIFdiU1mx64pReYCHA1diUxnaypuTkYYl8F0D3TkZmQDA1djis8sXpbC5Ssnp2Xy35yrRiYUHG6mZWsZ+vkd/7uFow8Hpg9HqlBIHH4qiFDlfyhTLEtT/bOtF3lt/hq8ndeG25uXb5qC2kwBICCHqGr8uavqN7HRw9YMLm+HUH3BmLax5yvQ9dq7QdDAA1n6dwcYZTXo8RB0D3w58PakLs/86xW8en+F+db16T7L6ski4Cu5NTD42t0cnV/cm7nw4tj3/HI/i3s6NGP7RdqPrheUk6+xfjzfvaMlXOy7zUA9/7l26u8Q/jrxORCbw1c7LRdY5E5VkdB6bksmawxG8s+40N/L00hQW5CSlZ3HHkh30aupptJ9RcSzy7oKgU7CwKPjs99arqUZe++0Ye18fVOJn10UyBCaEEHVR/eZqVnkHd2h7H3R7ouj6g+eAjYN6bGkFATmbMp5eC/++w20u19nyRIgh+Mkr6kTBsv1fwIJgetz42ajY0kJDA1d7HukdiLOdNf2aqZOl/T3Uz3awtqS/xWGcMO4BsrGyoL2fG4vHd6BLgDvT7yh6eK2wvpR9l+OKvK8wL6w6YhT8AEz4ah+7Lt4sUPe3g9e4GpvKD3vDSvUZefOjZcjS/HKTHiAhhBAQ0Ae6PgH7loFfN+gyGdwD1Y0WEyOh4wTj+oF94dw/sD1nftChb2DgDNPPjj4BLe80nJ/9B/5+EYA+FxcSrJnPeaURAJYWxv8uf3V4CIGejoxo2wAAlwtrWGGzgEjFnYEZ75OGOmnZNl+uMkebwtN4WBcy/6eibT9/k+3nbxaYsHyrjCvX8kZtaVla/YRwUTbSAySEEEJdTn/7fJiVAI9uhLb3QqPOEDICuk4uuBFj3onUoAZK+z5Xjzs/Cvd8DR452ev/ew8yU9Tjo6vgx/FGt26yfYVPrRfhQQLW+YZ1XOyseaxPExq42gNgd0rtMfLVxLHdZxEa1J4QWyvjYKCo4GD1U70KfJ3CdAt0L1nFUsib56w0srWG5fWpmUU/Q1NoH5fIJQGQEEKI0vNqUXDX6es5e+c06gyt74I7PjRcO5az/9CuJYACvh3hfsPw1+2W+xhjuUPNWh9xSF2Wb4ImwbC6yjP+GO016vJ7W+v8PUDGAxxWeQKrNo1cS/INAZh7Vxujpfhl8cTKAywOPa8/z7uP0Fc7LjN9zYkCCV5Nycw2TLDOu9pOlI0EQEIIIcrmziUw8W/o96qhzMIamg1Tj/3yZIq/dgDiwyA6J7B54Fd12C2P5ppw6ikJ8PVwWNob4vIts0+NQxOrBhLbteo+PP0t1aAr/xBY/snV9fKl7DDVPxL6Yr8CZU3qO/GvifLS2HAymg82GTacDMuzgm322lOs3HOVwGnrit1AMe+8n5QMCYDKSwIgIYQQZWNhoU6G7vUCNGivbpQ4+jN1YjWAlS2M+0E9PvEb/P2Sety4Jzh6qJOq7/9F/7hgi2u0Sjugrk4DOGa4BkD4XgAyXQPx63EPAJ2srwAwtJWPUVWHfL02+Tc+NLU6y8fFzuTXdKugjPdhsancTM5g3xXTE633X4kjKiG9QH6zXHkDoFGf7GTf5TgS07MYtmibUYAFBUcsRUESAAkhhCgfGweYvAWeP6rOHcrLt6P6np0O5zeoxx0eMFxvNgSeUROztre4xISodw3XbuZL03FOXWFmEzyAgLZq71FP+zB+eKyrPkdYrvw9QC721sV+DVsrC54fGKw/b+WrZhO3sbLQp+AozN9Tehf7/L4LtvDUdwcLvb582yW6zw1l6KJt7Divrh7T6RT+OhrJkfB4MrKNe33uW7abRZvOcyYqyWiITZSMBEBCCCHKz6KQXycuDQzHGgu4+0vokG/ukHsTMhUTk5Zzhru4tBU+7QEHV6jnLe4A71ZgYYVF6k16OkaiWfsC7PlMf2v+AOiD+9rR0M2e+Xe1hv1f0DrNOBmsvbUlVpYW/G9wMzZP7ct9nRux9MFO+uuzR7Xmu0e7mfyKXQPcaeVbsnlF+6/cKvRa6JkYAC7eSOHBL/dyIymDredieO7Hw4z+ZCdhcWkF7tl3pey7YVe0bK2u2MnZ1YksgxdCCFG57vgQzqyDMUvVlBz5WVoTjzNexBuX3zgLyTfgh3GQnfPLP+QOaHKbOsbj1UKdLL12KkQcUK836Q/Xj1H//BYcGExqzjL5Fg1c2PnaANj9CWx4nac19ixlKZmoPUP/vdxf/7FNvZyZf087o6bYWFnQO9hE24H07MqZjzP6k53cSDbsLXTmemKBOiciDGX5J1JfuZlCbEpmoYlfK9qYT3dxNTaF3dMG4ljOieNVQXqAhBBCVK7Oj8CDv5oOfnLY950CgNKoC8y4BbYu6rDZP6+owY+jl5rQdex3hgkuns3V99zgB+Dwd/D749ie+JHHrdaq1ZzyzOE5sw4AOyWNrhbqrsnfPNIVr0Lm/5RE7oqs0e19AYWHuvtzZztf6jkUP+xWlIj4NKPkslfjik7/kZ5lqHs9IZ3+72/l7s92cSEmuVztKIksrY7jEQkkpmdzOCy+0j+vIkgAJIQQwuycb/sf3PsNmnu+VofTGndXL5xcrb73fE7duTrv7F5nn4IP2v2x/nCc6ynmh1xgX9Nv1c0cM1Mh8pD++uOWaoCUu9t0WeXuQTQ/+BRX7B5gZsg1Fo/vwOEZQwrNOl8WmcXs/px3eX1eO87f0B/fTM5g0eZzRMYXHE7LdT46yeQO1gA3kjJMDnPl3QW7sNHQ6qaGNFMIIUStZmEBrUYbMtYPfdf4ept7Ct7jVHSyT5+UM9x3ZQYWZ/5UV6Ed+R6yDL0oPSxPM31oQJma+/LQ5iwZ34FAT0feu7stADZ/PQ2A1U9jQacGK2tLMDm6ouRPxZHrSs6y+5ORCXR+ezOLNp/n0W8OkJmt47s9V7l8MwWdTtEPoQ3+cBv3L9/L1dgUPtt6kUEf/MeNpAxuJGXQ5Z3N9H5vS4HPiE5M1x8nptWMeUDVf5BOCCFE3eMZDH1fhm0LIGgguPgWrOPkbTi2dlB3rT7+C/R9BU7/BTdOG64nXIPIw+rx0LmwawnWSZE8GpBvErFOBzsXgaKDPi8WWE/+8f0d2Hgymkd7B2JnbcnIdibaBXD6D2g1hmbezrw0pBnvbzxnul4+Q1p6czU2lbPRScVXzueFVUdMlq8+dI2k9GwOXDUsvz99PZGVe64yZ+0pAJp7O+PhZMO3jxj2bnrqu0Ocypl39MPeMJp6OQEQl5JZ4DOiEw3BV2E9UdWN9AAJIYSonvq/DuN+VCdPm+KcZ4VZ/RAY9Qk8uglue12dK+TbwXD92gH9PkK0Gg3+PdXjb0ZCVs5wkKLAxjcg9C34dw4c/7XAR97R1pfF4ztglz8rfXa+oCBsj/6wYT17/fFrw0OK+sb0DPLgo/Hti6xTmMLm+iSmZ/PbITUBa16HrhpWpJ2NTmLXxVjiUg3f41SeSddWlhoUCt+oMe+QWmKaBEBCCCFE2VlYQMjthQ91+feCLo+pO08PnKFuvOjXVe218WwKj2+FEQvVurkTpf26q71JuQEQwJZ31PfzG2HPp4byXR+pQdG5DeoS/KLSVSRHGZ/HnNIfjmzry3MDmvLDY914om8T9r0+0OQjugTU457OfoT4uPDh2HYm61QkU/nSIuPTTdSEqIR08m5UnX+zxrA8E7QTJAASQgghKpGllRrg3L+qYHLWXIH50li0Gq2+tx0Lbv7q8em/1PdTf+TUuQus7NUl9ld2wKoH4a/nDfsQmZKULwC6adiY0MrSgheHNKdnU080Gg1eLnZseKGvUfW1z/Xm5yd66POO5c9llpdrMZs69mjiAYCzXdGzXPLvlQSwcONZk3VX7rlKbJ4l+en5JmRfiU3RH+cPgDKzdTzwxR7m/XOmyPZUNQmAhBBC1F6ewTB4Dti7q6k6WudMprZ1gqd2goUV3LoC4fvg5O/qtU4TDYHSvs9BmzMstPYF2LkYsk1MNs4NgOoFGs5N1cvR3MeZEW0b4GBjyY5Xb6N1Q1ej9BzWVoX/ejYVAPm5G4bZhrbyZtXj3U3mNsvr291XC5RtP2969RfAhpOGIC8jXzLW8Dw9QBlZxsHRhpNR7LwQy9L/LhbZnqomk6CFEELUbr2mqK/8bJ3BuzVcPwK/PaquEGvUFQL7qpOmj/4Ip/80vmfTdMhMVucZ5ZUbAHm3ygl+0tRneAQV2qwl4zqQka0zORSVP8Bo09CV4xEJgOmemxY+LoTn7BTtaGtFt5xeoIq055JhEnVuD9AX2y9Rz8GGhDwrv7LyDY8VtjrN3CQAEkIIUXc16qIGQPFh6nnP59Q5RLn7EJly8Bto2AnOrlMTwUafgH9eVq+5+qlL+W+eg/ir4N7EeCXZqT/gz+dg9FIsQm43GfwA9AmuTwNXO64nqHNybmten37N6uPtYsvhsHjORBmvEgvycoJT0QD6YTQACw0Uk2S+TNKztFy+mcLbf6sr7awsDN8xMycAOhWZyORvD2BlabiWrdVhVYF7I5VH9WiFEEIIYQ5dHgXLnJ2ig4dAi5HqsXsTdffpXI26wG1vqMfJUfDDfeqcoDVPq3OEcnm3UlN0AGx7H+YHwu48E6t/ngDpCfDT+CInVTvaWrHj1QH6c50CLw1tzkM9Aph+R0se7N6YaXlWlDXxdNQfO+QJgL5/rGAg1795+TZ+BFi69aJRz052nigrtwfoxV+OEhGfZrT6LC2rctKGlIUEQEIIIeourxbwxHZ4eg/c/7Oht0ajUYOeXN2fhn6vqLnI8grbZXzu3Qr8cpKmXt0JabdgwzSID4eUfPNrYi8U2TTLPL0qeZeg13O04e3RbWjTyJCANShnjx4AJ1tDr1KPIA/m39PW6LkDQ4reQLIkfjl4jfuW7TZ5bcPJaFbtD+O0idxlaZla1p+I4q+jkeVuQ3nJEJgQQoi6zauQvXnyZrL3aaO+3/5+zj05iVjPrVfPnXzUidO+HdRNFPM7/oshMMp17GcY8EaRTQvwcOBKbCrDWzcocC3vXkS+roZJ0A75VpDZ5plQvWfaQK7dKjqnWC53RxuSM7KZObIlb/x+okT35Hr1t+Mmy1cfjtCvBmvT0JWAPD1XVU0CICGEEMKU5sNh/xfg0lBdTQZqUDTue/U48bo6pyd4sPFkZ5+2BZ91aSvYuRiX7VsGfV9S9y8qxLrn+xCTmGEyUOjg58akXgE0qe+EWxGJV/OuyqrvbIu3iy3PDWjKkn9N90Btf+U26jna4GRrhVanYGmhISwulWX/XSr0M0oq71L43ZdiJQASQgghqp2mg+CBX00HNKAGQ92fLFhuZaOuLovO02tyeRtcP6oe93sNDn0LSZGwfhokR6sv9yA1B5qjYQWXg40VAZ6mf1VrNBpmjmylPw/0dCQ+NZMm9Y2Dioxsw7yb3GG1F4c0LzQA8naxwyan1yi3/rThLYhNzuTXg9dM/yzK4MCVW4zv2rjCnldaMgdICCGEKEzwYHD2Lr5efuO+V3eofuh3ddNFFEiPV6+1udeQ3PXAl3BmLVzbD8d+gr+mFLl/UFE2/q8vu6cN1Genz9XRv16Jn9HA1RD85PfumDYFyvLPLyqNGXe0LPO9FUECICGEEKKi1QvI2aF6AAyaZSi3dVGHyzo+DJYmhr7OrIUvBoG2hOkkdFq4EAqJkVhbWhTMUQa08nXlt6d6smeacQqO5t7OhuY6WDP9jpa8f2/hKThsrCyYf7ch4LmvcyMG5JlQ7elkU7I253AtYtiuKsgQmBBCCFGZXHzV3qBz66Hr44ZcZRPWQMRBNaeZtYOahPXsOog6piZv9e+hptRYcQc06gz3fgPHf1Z3rL7rc7CvBxvfVPOXeQTDM3vBwvS+Qp1M9AKteaYXF2KS2Xo2hlHtG9LYw6HYr3JfFz9e+e0YAB0b18PD0RD05N3JuiaQAEgIIYSobPeuUJfBu/kZyvx7GidlHfcDfDkEru2DxAi17NQadd+hM2vhbS9QcubznFgNHR4y5CeLPa/mLWtSdPqLvOxtLGnTyNVoOX1J/PZUT/ZciuXezn5GQY+pHapz5d3JurqQITAhhBCislnbGwc/pmg06tAZqGk0QF1qn0vJs4ngzXNwdYeaviNXrCEBK9ePweHvis5gX0ad/OvxzG1N9ROknx8YjIudFVMHNyv0nqf7F54SxFwkABJCCCGqC9eG6vuWdyEtHqIK2X8n6gQc+dG47NYV9V2nhWV94I9n1CG1Sva/wc04MmMIzX2cC61jb2PJwz38K70tpSEBkBBCCFFdeOb0omgz1DlBcTl77/R5EQa9Bc/sV8+v7lDnAwG0G6++x11W3y9vMzzv3IbKbzNgYaHBuogcX7ZWlrw1qrXRpozmVn1aIoQQQtR1LUcZjg98BSjg3AAGzoDeL6gbMjr7Guq0uTdnmT3q5GlQd5jOdWW74TjuElz6r/RtSrimbuSoKzqPl02eAGj10z2NrtVzVFd8ebkUvuljVZMASAghhKgubBxhYr5hq9wEraDOE+r1vHpsX08NjBp1Bo2FmtH+Zs5k6Fxxl9SeoexMWDESvr0TZrnCzw8b5gelxEJmIekxFAVW3gXfjlJXnBUhbw+Qp6Mh0PF0siHER90F+7MHOhFU35FlD3Uq+udQBWQVmBBCCFGd+PcEO1c1a7xLQxg40/h6tyfUnGPugeCUsw+Pd2u1B+jjzjmVNODVEmJOqr1AjvUhMc8uzqfWqHOGYi/CD/eCoxc8u0/93Lz2fwE3z6rHez5Vh+IcPU0229rSsCLM2spw/O0jhhxorRu6Evpi/5L/LCqR9AAJIYQQ1YlGA/f/Ai3uhEnrwNap4PXG3QzBDxiGwXJ5tYCQ29Xjo6vU/Ybyu7xNnSSt6NSl9kdXGV+/cQ7WvWxcduK3IpptCHosNRp2vHobqx7vTktfl0LvMScJgIQQQojqpnE3GLvSsCy+OF0fh/6vG86b3KYGUKBOmN62QD12zBM0/TVFTcWRK+8xwKUtgAK+HWHI22rZ0R/V4bS9n0PYHqPqefcBcrG3plE9B7o18aC6kgBICCGEqOmsbKD/q3D3l9B0MHR5FHzagJ9h+AmNJTy2CZ7cAZjYtfnGGUhPNJyH7Vbfmw9XV5pZWEHkYVjzJPzzMnwzUh1Cy2Fnbcmm53typPMG7PZ8VDnfswJJACSEEELUFm3ugQd/VfONaTRw9xdq4AMwbJ7ao+TTBib+DQPeBDTQ6i6wd1frJISr7zqtuvILILCvOu8neIh6njsMps2E03+px4oCV3YQfG01bie+UZfwh+8z3ca0W4aNHs1IAiAhhBCitnJrrAY7d38JnR8xlAf0gr4vwwvHYMxStR7Ar4+ogU/EQTVQsXOFhjkTq9uNK/j8qzvVYGnf57BiBPw91XDt6I8F6wMc/h4+bA3rp1XIVywrWQUmhBBC1Gb+PQq/lhv41POH60fUYbBvRxl6hJrcBpY5oUKz4eDeRF1a7+YP8Vfh/EaY46lOpM7vwFdq8NThAePyoz8BCng0Le83KxfpARJCCCHqurb5enfS4sDSBro9aSizsoEJf8IdH8LjWw3lpoIfm5y0GH88DetfN+xSHXUCoo+rz241pkK/QmmZPQD65JNPCAgIwM7Ojm7durFvXyFjhsDJkye5++67CQgIQKPRsGjRogJ1Zs2ahUajMXqFhIRU4jcQQgghariQ2+Gh36HnFLBxgib94dkDBXuP3PzUoTQH94JBU16PbTYc7/kElnRS5wnlzhkKHqI+w4zMGgCtWrWKqVOnMnPmTA4dOkS7du0YOnQoMTExJuunpqbSpEkT5s2bh4+PT6HPbdWqFdevX9e/duzYUWhdIYQQQgBBA2DIHHgtHCb8oQ6LFWXoOzB4troJY35eIdA4TzoMRQu7FsN/89TzpgMrrt1lZNYA6IMPPmDy5MlMmjSJli1bsnTpUhwcHPjqq69M1u/SpQsLFixg3Lhx2NoWnk/EysoKHx8f/cvT0/SulUIIIYTIx6KEoYGjp5qWY9I6GDAduj+jlg97T33Pu1EjwKYZhuOAPuVvZzmZbRJ0ZmYmBw8eZNo0wyxwCwsLBg0axO7du8v17PPnz+Pr64udnR09evRg7ty5NG7cuND6GRkZZGRk6M8TExMLrSuEEEKIPOxcoe9L6nGv59W0GwDOhYzU9HlJTepqZmbrAbp58yZarRZvb2+jcm9vb6Kiosr83G7durFixQrWr1/PZ599xuXLl+nTpw9JSUmF3jN37lxcXV31Lz8/vzJ/vhBCCFFnOXsbepB6T4WQO6DLZMP1e76CgdPN07Z8at0y+OHDh+uP27ZtS7du3fD39+fnn3/m0UcfNXnPtGnTmDrVsHdBYmKiBEFCCCFEeTh7w7jvISNZ3UHaPRBajjZ3q/TMFgB5enpiaWlJdHS0UXl0dHSRE5xLy83NjWbNmnHhwoVC69ja2hY5p0gIIYQQZWTrBJNDzd2KAsw2BGZjY0OnTp0IDTX8UHQ6HaGhofToUcSmTaWUnJzMxYsXadCgQYU9UwghhBA1m1mHwKZOncrDDz9M586d6dq1K4sWLSIlJYVJkyYBMGHCBBo2bMjcuXMBdeL0qVOn9McREREcOXIEJycnmjZVd5R86aWXGDlyJP7+/kRGRjJz5kwsLS0ZP368eb6kEEIIIaodswZAY8eO5caNG8yYMYOoqCjat2/P+vXr9ROjw8LCsMizHC8yMpIOHTroz99//33ef/99+vXrx9atWwG4du0a48ePJzY2lvr169O7d2/27NlD/fr1q/S7CSGEEKL60iiKopi7EdVNYmIirq6uJCQk4OLiYu7mCCGEEKIESvP72+ypMIQQQgghqpoEQEIIIYSocyQAEkIIIUSdIwGQEEIIIeocCYCEEEIIUedIACSEEEKIOkcCICGEEELUORIACSGEEKLOkQBICCGEEHWOBEBCCCGEqHPMmgususrNDpKYmGjmlgghhBCipHJ/b5cky5cEQCYkJSUB4OfnZ+aWCCGEEKK0kpKScHV1LbKOJEM1QafTERkZibOzMxqNpsKem5iYiJ+fH+Hh4ZJktRLJz7nqyM+6asjPuWrIz7lqVObPWVEUkpKS8PX1xcKi6Fk+0gNkgoWFBY0aNaq057u4uMj/XFVAfs5VR37WVUN+zlVDfs5Vo7J+zsX1/OSSSdBCCCGEqHMkABJCCCFEnSMBUBWytbVl5syZ2NramrsptZr8nKuO/Kyrhvycq4b8nKtGdfk5yyRoIYQQQtQ50gMkhBBCiDpHAiAhhBBC1DkSAAkhhBCizpEASAghhBB1jgRAVeiTTz4hICAAOzs7unXrxr59+8zdpBpj7ty5dOnSBWdnZ7y8vBg9ejRnz541qpOens4zzzyDh4cHTk5O3H333URHRxvVCQsLY8SIETg4OODl5cXLL79MdnZ2VX6VGmXevHloNBpeeOEFfZn8nCtOREQEDz74IB4eHtjb29OmTRsOHDigv64oCjNmzKBBgwbY29szaNAgzp8/b/SMuLg4HnjgAVxcXHBzc+PRRx8lOTm5qr9KtaXVapk+fTqBgYHY29sTFBTEnDlzjHJFyc+59LZt28bIkSPx9fVFo9GwZs0ao+sV9TM9duwYffr0wc7ODj8/P+bPn19xX0IRVeKnn35SbGxslK+++ko5efKkMnnyZMXNzU2Jjo42d9NqhKFDhypff/21cuLECeXIkSPK7bffrjRu3FhJTk7W13nyyScVPz8/JTQ0VDlw4IDSvXt3pWfPnvrr2dnZSuvWrZVBgwYphw8fVtatW6d4enoq06ZNM8dXqvb27dunBAQEKG3btlWef/55fbn8nCtGXFyc4u/vr0ycOFHZu3evcunSJWXDhg3KhQsX9HXmzZunuLq6KmvWrFGOHj2q3HnnnUpgYKCSlpamrzNs2DClXbt2yp49e5Tt27crTZs2VcaPH2+Or1QtvfPOO4qHh4eydu1a5fLly8ovv/yiODk5KR999JG+jvycS2/dunXKG2+8oaxevVoBlN9//93oekX8TBMSEhRvb2/lgQceUE6cOKH8+OOPir29vbJs2bIK+Q4SAFWRrl27Ks8884z+XKvVKr6+vsrcuXPN2KqaKyYmRgGU//77T1EURYmPj1esra2VX375RV/n9OnTCqDs3r1bURT1f1gLCwslKipKX+ezzz5TXFxclIyMjKr9AtVcUlKSEhwcrGzatEnp16+fPgCSn3PFefXVV5XevXsXel2n0yk+Pj7KggUL9GXx8fGKra2t8uOPPyqKoij/b+/uYpo82ziA/1tKK2UixY4WNUzNCOBn0DpSMSYKEdEDdTijaUjVA4KCgropmSPqAc4jTVwyjMvUA1Gixg900QXBaTCIiIAwFZdsfkTp8AsBv2D2eg+cz+sjbq++ltba/y95kva5b+p1/4ntlT696cWLFwWA1NTUKHOOHj0qGo1Gbt682XvF+5Hp06fLwoULVec+//xzcTgcIsKcPeHVBshTmX7//fdiMplUzxurVq2S2NhYj9TNS2Be0NXVhdraWqSkpCjntFotUlJSUFVV5cPK/NeDBw8AABEREQCA2tpadHd3qzKOi4tDdHS0knFVVRVGjhwJi8WizElNTUV7ezt+/fVXL1b//svOzsb06dNVeQLM2ZNKS0ths9nwxRdfIDIyEgkJCfjhhx+U8T/++AMul0uVdb9+/ZCYmKjKOjw8HDabTZmTkpICrVaL6upq7y3mPTZ+/HiUl5fjypUrAICGhgZUVlYiLS0NAHPuDZ7KtKqqChMnToRer1fmpKamorm5Gffv33/nOvllqF5w584dPHv2TPWCAAAWiwWXL1/2UVX+y+12Iy8vD0lJSRgxYgQAwOVyQa/XIzw8XDXXYrHA5XIpc173O3gxRs+VlJTg/PnzqKmp6THGnD3n999/R1FREZYvX46vv/4aNTU1WLp0KfR6PZxOp5LV67J8OevIyEjVuE6nQ0REBLP+W35+Ptrb2xEXF4egoCA8e/YMhYWFcDgcAMCce4GnMnW5XBgyZEiPx3gxZjKZ3qlONkDkd7Kzs9HU1ITKykpfl/LBuXHjBnJzc1FWVoY+ffr4upwPmtvths1mw/r16wEACQkJaGpqwpYtW+B0On1c3Ydjz549KC4uxq5duzB8+HDU19cjLy8PAwYMYM4BjpfAvMBsNiMoKKjHTpk///wTVqvVR1X5p5ycHBw5cgQnTpzAoEGDlPNWqxVdXV1oa2tTzX85Y6vV+trfwYsxen6Jq7W1FWPGjIFOp4NOp8PJkyexefNm6HQ6WCwW5uwhUVFRGDZsmOpcfHw8rl+/DuC/Wf3b84bVakVra6tq/K+//sK9e/eY9d+++uor5OfnY+7cuRg5ciQyMjKwbNkyfPvttwCYc2/wVKa9/VzCBsgL9Ho9xo4di/LycuWc2+1GeXk57Ha7DyvzHyKCnJwcHDhwABUVFT3eFh07diyCg4NVGTc3N+P69etKxna7HY2Njar/dGVlZQgLC+vxQhSokpOT0djYiPr6euWw2WxwOBzKbebsGUlJST3+lMOVK1fwySefAACGDBkCq9Wqyrq9vR3V1dWqrNva2lBbW6vMqaiogNvtRmJiohdW8f579OgRtFr1S11QUBDcbjcA5twbPJWp3W7HqVOn0N3drcwpKytDbGzsO1/+AsBt8N5SUlIiBoNBduzYIRcvXpTMzEwJDw9X7ZShf7Zo0SLp16+f/PLLL9LS0qIcjx49UuZkZWVJdHS0VFRUyLlz58Rut4vdblfGX2zPnjJlitTX18uxY8fk448/5vbs/+HlXWAizNlTzp49KzqdTgoLC+W3336T4uJiMRqNsnPnTmXOhg0bJDw8XA4dOiQXLlyQGTNmvHYrcUJCglRXV0tlZaXExMQE9PbsVzmdThk4cKCyDX7//v1iNptl5cqVyhzm/PY6Ojqkrq5O6urqBIBs3LhR6urq5Nq1ayLimUzb2trEYrFIRkaGNDU1SUlJiRiNRm6D90ffffedREdHi16vl88++0zOnDnj65L8BoDXHtu3b1fmPH78WBYvXiwmk0mMRqPMmjVLWlpaVI9z9epVSUtLk5CQEDGbzbJixQrp7u728mr8y6sNEHP2nMOHD8uIESPEYDBIXFycbN26VTXudruloKBALBaLGAwGSU5OlubmZtWcu3fvyrx58+Sjjz6SsLAwWbBggXR0dHhzGe+19vZ2yc3NlejoaOnTp48MHTpUVq9erdpazZzf3okTJ177nOx0OkXEc5k2NDTIhAkTxGAwyMCBA2XDhg0eW4NG5KU/h0lEREQUAPgZICIiIgo4bICIiIgo4LABIiIiooDDBoiIiIgCDhsgIiIiCjhsgIiIiCjgsAEiIiKigMMGiIjoDWg0Ghw8eNDXZRCRh7ABIqL33vz586HRaHocU6dO9XVpROSndL4ugIjoTUydOhXbt29XnTMYDD6qhoj8Hd8BIiK/YDAYYLVaVceLb4TWaDQoKipCWloaQkJCMHToUOzbt0/1842NjZg8eTJCQkLQv39/ZGZmorOzUzVn27ZtGD58OAwGA6KiopCTk6Mav3PnDmbNmgWj0YiYmBiUlpb27qKJqNewASKiD0JBQQHS09PR0NAAh8OBuXPn4tKlSwCAhw8fIjU1FSaTCTU1Ndi7dy+OHz+uanCKioqQnZ2NzMxMNDY2orS0FJ9++qnq31i3bh3mzJmDCxcuYNq0aXA4HLh3755X10lEHuKxr1UlIuolTqdTgoKCJDQ0VHUUFhaKiAgAycrKUv1MYmKiLFq0SEREtm7dKiaTSTo7O5Xxn376SbRarbhcLhERGTBggKxevfofawAg33zzjXK/s7NTAMjRo0c9tk4i8h5+BoiI/MKkSZNQVFSkOhcREaHcttvtqjG73Y76+noAwKVLlzB69GiEhoYq40lJSXC73WhuboZGo8GtW7eQnJz8rzWMGjVKuR0aGoqwsDC0trb+v0siIh9iA0REfiE0NLTHJSlPCQkJeaN5wcHBqvsajQZut7s3SiKiXsbPABHRB+HMmTM97sfHxwMA4uPj0dDQgIcPHyrjp0+fhlarRWxsLPr27YvBgwejvLzcqzUTke/wHSAi8gtPnz6Fy+VSndPpdDCbzQCAvXv3wmazYcKECSguLsbZs2fx448/AgAcDgfWrFkDp9OJtWvX4vbt21iyZAkyMjJgsVgAAGvXrkVWVhYiIyORlpaGjo4OnD59GkuWLPHuQonIK9gAEZFfOHbsGKKiolTnYmNjcfnyZQDPd2iVlJRg8eLFiIqKwu7duzFs2DAAgNFoxM8//4zc3FyMGzcORqMR6enp2Lhxo/JYTqcTT548waZNm/Dll1/CbDZj9uzZ3lsgEXmVRkTE10UQEb0LjUaDAwcOYObMmb4uhYj8BD8DRERERAGHDRAREREFHH4GiIj8Hq/kE9Hb4jtAREREFHDYABEREVHAYQNEREREAYcNEBEREQUcNkBEREQUcNgAERERUcBhA0REREQBhw0QERERBRw2QERERBRw/gM0TkzPg36N/QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "deadlift_train_losses = []\n",
    "deadlift_test_losses = []\n",
    "\n",
    "epochs = 1000\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    deadlift_model.train()\n",
    "    deadlift_optimizer.zero_grad()\n",
    "    deadlift_outputs = deadlift_model(deadlift_X_train)\n",
    "    deadlift_loss = deadlift_criterion(deadlift_outputs, deadlift_y_train)\n",
    "    deadlift_loss.backward()\n",
    "    deadlift_optimizer.step()\n",
    "    deadlift_train_losses.append(deadlift_loss.item())\n",
    "    \n",
    "    deadlift_model.eval()\n",
    "    with torch.no_grad():\n",
    "        deadlift_test_outputs = deadlift_model(deadlift_X_test)\n",
    "        deadlift_test_loss = deadlift_criterion(deadlift_test_outputs, deadlift_y_test)\n",
    "        deadlift_test_losses.append(deadlift_test_loss.item())\n",
    "\n",
    "plt.plot(range(1, epochs+1), deadlift_train_losses, label='Train Loss')\n",
    "plt.plot(range(1, epochs+1), deadlift_test_losses, label='Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "f733762f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.15430787205696106\n"
     ]
    }
   ],
   "source": [
    "deadlift_model.eval()\n",
    "with torch.no_grad():\n",
    "    deadlift_test_outputs = deadlift_model(deadlift_X_test)\n",
    "    deadlift_test_loss = deadlift_criterion(deadlift_test_outputs, deadlift_y_test)\n",
    "    print(f'Test Loss: {deadlift_test_loss.item()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "262be563",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 5.2127e-01,  2.1887e-01, -3.9819e-01,  9.9971e-01,  5.2453e-01,\n",
      "         2.0736e-01, -3.7531e-01,  9.9929e-01,  5.2565e-01,  2.0755e-01,\n",
      "        -3.7533e-01,  9.9924e-01,  5.2649e-01,  2.0764e-01, -3.7534e-01,\n",
      "         9.9939e-01,  5.1365e-01,  2.0457e-01, -4.2631e-01,  9.9953e-01,\n",
      "         5.0681e-01,  2.0278e-01, -4.2641e-01,  9.9951e-01,  4.9883e-01,\n",
      "         2.0077e-01, -4.2652e-01,  9.9953e-01,  5.0255e-01,  2.0536e-01,\n",
      "        -2.1498e-01,  9.9841e-01,  4.6710e-01,  1.9748e-01, -4.4688e-01,\n",
      "         9.9920e-01,  5.1085e-01,  2.2838e-01, -3.3054e-01,  9.9969e-01,\n",
      "         4.9841e-01,  2.2584e-01, -3.9803e-01,  9.9978e-01,  4.9899e-01,\n",
      "         2.7553e-01,  3.7108e-03,  9.9977e-01,  3.7568e-01,  2.5603e-01,\n",
      "        -5.1498e-01,  9.9981e-01,  5.2298e-01,  3.5458e-01,  1.2810e-01,\n",
      "         8.2508e-01,  3.8221e-01,  3.5001e-01, -5.4992e-01,  9.9219e-01,\n",
      "         5.6086e-01,  4.2778e-01,  2.7157e-02,  8.9767e-01,  4.2946e-01,\n",
      "         4.3675e-01, -5.6683e-01,  9.8579e-01,  5.6860e-01,  4.4655e-01,\n",
      "         3.7685e-03,  8.4089e-01,  4.2329e-01,  4.5967e-01, -6.2843e-01,\n",
      "         9.7457e-01,  5.6514e-01,  4.4692e-01, -5.1687e-02,  8.5450e-01,\n",
      "         4.4327e-01,  4.5998e-01, -6.5385e-01,  9.7377e-01,  5.6015e-01,\n",
      "         4.4191e-01, -8.1111e-04,  8.4523e-01,  4.4992e-01,  4.5316e-01,\n",
      "        -5.7451e-01,  9.5975e-01,  4.4709e-01,  4.2512e-01,  1.5441e-01,\n",
      "         9.9745e-01,  3.7011e-01,  4.2289e-01, -1.5454e-01,  9.9834e-01,\n",
      "         4.4967e-01,  5.4029e-01,  2.7037e-01,  7.0281e-01,  3.8377e-01,\n",
      "         5.4419e-01, -1.4619e-01,  9.6444e-01,  4.4387e-01,  6.5172e-01,\n",
      "         4.9441e-01,  7.1400e-01,  3.5767e-01,  6.6880e-01, -4.4155e-02,\n",
      "         9.6885e-01,  4.2713e-01,  6.6859e-01,  5.0687e-01,  6.6303e-01,\n",
      "         3.3894e-01,  6.8789e-01, -4.8430e-02,  9.0057e-01,  5.2357e-01,\n",
      "         6.8002e-01,  3.8199e-01,  8.0194e-01,  4.3034e-01,  7.0508e-01,\n",
      "        -2.5747e-01,  9.6145e-01])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 0., 0., 1.])\n",
      "예측된 데이터: tensor(7)\n",
      "예측 확률: ['0.0000', '0.3058', '0.0000', '0.0765', '0.0000', '0.0855', '0.0000', '0.5322']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 5.2011e-01,  1.4107e-01, -1.9416e-01,  1.0000e+00,  5.3828e-01,\n",
      "         1.2640e-01, -1.6566e-01,  1.0000e+00,  5.4625e-01,  1.2688e-01,\n",
      "        -1.6571e-01,  1.0000e+00,  5.5417e-01,  1.2722e-01, -1.6563e-01,\n",
      "         1.0000e+00,  5.0789e-01,  1.2571e-01, -1.5703e-01,  1.0000e+00,\n",
      "         4.9793e-01,  1.2570e-01, -1.5694e-01,  1.0000e+00,  4.8943e-01,\n",
      "         1.2575e-01, -1.5685e-01,  1.0000e+00,  5.6764e-01,  1.3430e-01,\n",
      "        -6.9099e-03,  1.0000e+00,  4.7765e-01,  1.3286e-01,  2.8674e-02,\n",
      "         1.0000e+00,  5.3819e-01,  1.5646e-01, -1.3769e-01,  1.0000e+00,\n",
      "         5.0079e-01,  1.5617e-01, -1.2672e-01,  1.0000e+00,  6.5037e-01,\n",
      "         2.3054e-01,  2.3351e-02,  1.0000e+00,  4.0671e-01,  2.2573e-01,\n",
      "         6.9338e-02,  9.9999e-01,  6.8162e-01,  3.2684e-01,  2.0843e-02,\n",
      "         9.9336e-01,  3.4248e-01,  3.1988e-01,  9.4674e-02,  9.8884e-01,\n",
      "         6.7317e-01,  4.1433e-01, -1.5581e-01,  9.9503e-01,  3.3388e-01,\n",
      "         4.0946e-01, -5.2549e-02,  9.7921e-01,  6.8279e-01,  4.4379e-01,\n",
      "        -2.1903e-01,  9.8994e-01,  3.2419e-01,  4.3951e-01, -1.1800e-01,\n",
      "         9.6182e-01,  6.6031e-01,  4.3835e-01, -2.7812e-01,  9.8979e-01,\n",
      "         3.4946e-01,  4.3558e-01, -1.7638e-01,  9.6270e-01,  6.5240e-01,\n",
      "         4.2994e-01, -1.8160e-01,  9.8686e-01,  3.5847e-01,  4.2570e-01,\n",
      "        -8.2065e-02,  9.6179e-01,  5.8438e-01,  4.3249e-01, -8.4124e-04,\n",
      "         9.9996e-01,  4.4190e-01,  4.2815e-01,  6.4534e-04,  9.9996e-01,\n",
      "         5.8002e-01,  5.9552e-01, -1.9521e-02,  9.9060e-01,  4.1082e-01,\n",
      "         5.8936e-01, -3.6063e-02,  9.9203e-01,  5.8610e-01,  7.4716e-01,\n",
      "         9.5683e-02,  9.9109e-01,  3.8076e-01,  7.4356e-01,  1.0789e-01,\n",
      "         9.9213e-01,  5.5517e-01,  7.6739e-01,  9.0016e-02,  8.7710e-01,\n",
      "         4.1665e-01,  7.6296e-01,  1.0547e-01,  8.6637e-01,  6.1381e-01,\n",
      "         8.0071e-01, -1.4571e-01,  9.8500e-01,  3.1489e-01,  7.9298e-01,\n",
      "        -1.2318e-01,  9.8599e-01])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 1., 0., 0.])\n",
      "예측된 데이터: tensor(5)\n",
      "예측 확률: ['0.0000', '0.3190', '0.0000', '0.2528', '0.0000', '0.3610', '0.0000', '0.0671']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 0.5173,  0.1426, -0.2072,  1.0000,  0.5345,  0.1305, -0.1805,  1.0000,\n",
      "         0.5446,  0.1309, -0.1805,  1.0000,  0.5528,  0.1315, -0.1805,  1.0000,\n",
      "         0.5038,  0.1305, -0.1753,  1.0000,  0.4945,  0.1310, -0.1752,  1.0000,\n",
      "         0.4854,  0.1315, -0.1751,  1.0000,  0.5669,  0.1405, -0.0229,  1.0000,\n",
      "         0.4796,  0.1403, -0.0037,  1.0000,  0.5382,  0.1595, -0.1489,  1.0000,\n",
      "         0.5021,  0.1595, -0.1429,  1.0000,  0.6544,  0.2378,  0.0237,  1.0000,\n",
      "         0.4118,  0.2325,  0.0543,  1.0000,  0.6868,  0.3262,  0.0742,  0.9925,\n",
      "         0.3363,  0.3212,  0.1127,  0.9835,  0.6839,  0.4115, -0.0254,  0.9883,\n",
      "         0.3321,  0.4087,  0.0164,  0.9480,  0.6913,  0.4378, -0.0682,  0.9757,\n",
      "         0.3261,  0.4343, -0.0232,  0.9088,  0.6736,  0.4335, -0.1213,  0.9757,\n",
      "         0.3474,  0.4323, -0.0686,  0.9095,  0.6644,  0.4248, -0.0468,  0.9710,\n",
      "         0.3550,  0.4237, -0.0049,  0.9091,  0.5873,  0.4315,  0.0041,  0.9999,\n",
      "         0.4470,  0.4279, -0.0046,  0.9999,  0.5833,  0.5942, -0.0425,  0.9862,\n",
      "         0.4126,  0.5906, -0.0775,  0.9884,  0.5851,  0.7466,  0.0537,  0.9891,\n",
      "         0.3822,  0.7441,  0.0488,  0.9910,  0.5515,  0.7668,  0.0460,  0.8679,\n",
      "         0.4180,  0.7640,  0.0440,  0.8443,  0.6146,  0.7987, -0.1885,  0.9822,\n",
      "         0.3143,  0.7914, -0.1906,  0.9848])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 1., 0., 0.])\n",
      "예측된 데이터: tensor(1)\n",
      "예측 확률: ['0.0000', '0.3738', '0.0000', '0.2112', '0.0000', '0.2664', '0.0000', '0.1486']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 0.6926,  0.4154, -0.6925,  0.9997,  0.7008,  0.4036, -0.6606,  0.9995,\n",
      "         0.7007,  0.4024, -0.6608,  0.9995,  0.7002,  0.4009, -0.6612,  0.9996,\n",
      "         0.6966,  0.4023, -0.7235,  0.9996,  0.6935,  0.4003, -0.7236,  0.9996,\n",
      "         0.6899,  0.3980, -0.7237,  0.9996,  0.6703,  0.3899, -0.4801,  0.9992,\n",
      "         0.6586,  0.3877, -0.7641,  0.9995,  0.6743,  0.4189, -0.6182,  0.9998,\n",
      "         0.6682,  0.4169, -0.7014,  0.9999,  0.6042,  0.4196, -0.2710,  0.9998,\n",
      "         0.5089,  0.4115, -0.7439,  0.9998,  0.5721,  0.4863, -0.1224,  0.8104,\n",
      "         0.4443,  0.4936, -0.7885,  0.9900,  0.5495,  0.5547, -0.2117,  0.8464,\n",
      "         0.4235,  0.5757, -0.7870,  0.9749,  0.5415,  0.5729, -0.2334,  0.7792,\n",
      "         0.4132,  0.5920, -0.8758,  0.9542,  0.5376,  0.5701, -0.3074,  0.7929,\n",
      "         0.4276,  0.5941, -0.8819,  0.9541,  0.5362,  0.5639, -0.2436,  0.7848,\n",
      "         0.4330,  0.5881, -0.7924,  0.9279,  0.4171,  0.4612,  0.1151,  0.9987,\n",
      "         0.3344,  0.4533, -0.1147,  0.9984,  0.5171,  0.5429, -0.0809,  0.7942,\n",
      "         0.3809,  0.5233, -0.5015,  0.9708,  0.4930,  0.6628, -0.0167,  0.7432,\n",
      "         0.4093,  0.6241, -0.2286,  0.9116,  0.4702,  0.6804, -0.0151,  0.6504,\n",
      "         0.3994,  0.6369, -0.2029,  0.8233,  0.5658,  0.6898, -0.1579,  0.7772,\n",
      "         0.4701,  0.6582, -0.3399,  0.9044])\n",
      "실제 타겟 데이터: tensor([1., 0., 0., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(4)\n",
      "예측 확률: ['0.0851', '0.0017', '0.0712', '0.0043', '0.8219', '0.0022', '0.0126', '0.0011']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 0.3585,  0.4348, -1.8838,  1.0000,  0.3733,  0.4199, -1.8532,  1.0000,\n",
      "         0.3836,  0.4180, -1.8532,  1.0000,  0.3926,  0.4157, -1.8537,  1.0000,\n",
      "         0.3442,  0.4200, -1.8513,  1.0000,  0.3346,  0.4180, -1.8514,  1.0000,\n",
      "         0.3259,  0.4156, -1.8513,  1.0000,  0.4076,  0.4048, -1.5341,  1.0000,\n",
      "         0.3105,  0.4059, -1.5273,  1.0000,  0.3752,  0.4406, -1.7522,  1.0000,\n",
      "         0.3429,  0.4404, -1.7498,  1.0000,  0.4926,  0.4370, -1.1616,  1.0000,\n",
      "         0.2387,  0.4354, -1.1138,  1.0000,  0.6038,  0.5262, -1.2339,  0.9983,\n",
      "         0.1371,  0.5225, -1.1683,  0.9942,  0.6241,  0.6261, -1.4695,  0.9951,\n",
      "         0.0735,  0.6152, -1.3927,  0.9819,  0.6362,  0.6527, -1.5735,  0.9827,\n",
      "         0.0491,  0.6389, -1.4852,  0.9487,  0.6043,  0.6518, -1.6292,  0.9851,\n",
      "         0.0709,  0.6403, -1.5527,  0.9537,  0.5962,  0.6461, -1.4990,  0.9820,\n",
      "         0.0821,  0.6322, -1.4277,  0.9497,  0.4420,  0.4866, -0.0075,  0.9999,\n",
      "         0.3057,  0.4842,  0.0078,  0.9999,  0.4387,  0.5855, -0.8374,  0.9370,\n",
      "         0.2627,  0.5871, -0.7395,  0.9493,  0.4039,  0.7393, -0.3559,  0.9491,\n",
      "         0.2826,  0.7359, -0.2739,  0.9664,  0.3807,  0.7582, -0.3304,  0.8290,\n",
      "         0.3076,  0.7550, -0.2505,  0.8043,  0.4280,  0.7827, -0.7420,  0.9488,\n",
      "         0.2297,  0.7790, -0.6546,  0.9547])\n",
      "실제 타겟 데이터: tensor([0., 0., 1., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(2)\n",
      "예측 확률: ['0.0000', '0.0000', '1.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_11625/2186968047.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "# 테스트 데이터셋에서 샘플 선택\n",
    "num_samples_to_test = 5  # 확인할 샘플의 수\n",
    "for i in range(num_samples_to_test):\n",
    "    sample_index = i\n",
    "    input_sample = deadlift_X_test[sample_index]  # 입력 데이터\n",
    "    target_sample = deadlift_y_test[sample_index]  # 실제 타겟(정답) 데이터\n",
    "\n",
    "    # 입력 데이터를 Tensor로 변환\n",
    "    input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n",
    "\n",
    "    # 모델에 입력하여 예측 생성\n",
    "    deadlift_model.eval()\n",
    "    with torch.no_grad():\n",
    "        prediction = deadlift_model(input_tensor.unsqueeze(0))  # unsqueeze를 사용하여 배치 차원 추가\n",
    "\n",
    "    # 예측 결과 출력\n",
    "    print(f\"샘플 {sample_index + 1}:\")\n",
    "    print(\"입력 데이터:\", input_sample)\n",
    "    print(\"실제 타겟 데이터:\", target_sample)\n",
    "    print(\"예측된 데이터:\", torch.argmax(prediction))  # 확률이 아닌 예측된 클래스를 출력\n",
    "    probabilities = prediction.squeeze().tolist()  # 텐서를 리스트로 변환\n",
    "    probabilities_str = [f\"{prob:.4f}\" for prob in probabilities]\n",
    "    print(\"예측 확률:\", probabilities_str)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "id": "11299320",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.400\n",
      "정밀도: 0.905\n",
      "재현율: 0.400\n",
      "F1 점수: 0.555\n"
     ]
    }
   ],
   "source": [
    "deadlift_model.eval()\n",
    "with torch.no_grad():\n",
    "    predicted_probs = deadlift_model(deadlift_X_test)\n",
    "\n",
    "threshold = 0.8  # 임계값 설정\n",
    "predicted_labels = (predicted_probs > threshold).numpy().astype(int)\n",
    "\n",
    "# 정확도 계산\n",
    "accuracy = accuracy_score(deadlift_y_test, predicted_labels)\n",
    "print(f\"정확도: {accuracy:.3f}\")\n",
    "\n",
    "# 정밀도 계산\n",
    "precision = precision_score(deadlift_y_test, predicted_labels, average='micro')\n",
    "print(f\"정밀도: {precision:.3f}\")\n",
    "\n",
    "# 재현율 계산\n",
    "recall = recall_score(deadlift_y_test, predicted_labels, average='micro')\n",
    "print(f\"재현율: {recall:.3f}\")\n",
    "\n",
    "# F1 점수 계산\n",
    "f1 = f1_score(deadlift_y_test, predicted_labels, average='micro')\n",
    "print(f\"F1 점수: {f1:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "id": "41d1840a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x4800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = multilabel_confusion_matrix(deadlift_y_test, predicted_labels)\n",
    "\n",
    "def plot_multilabel_confusion_matrix(confusion_matrix):\n",
    "    num_classes = len(confusion_matrix)\n",
    "    fig, axes = plt.subplots(nrows=num_classes, ncols=1, figsize=(8, 6 * num_classes))\n",
    "\n",
    "    for i in range(num_classes):\n",
    "        sns.heatmap(confusion_matrix[i], annot=True, cmap=\"Blues\", fmt=\"d\", ax=axes[i])\n",
    "        axes[i].set_title(f\"Class {i+1} Confusion Matrix\")\n",
    "        axes[i].set_xlabel(\"Predicted label\")\n",
    "        axes[i].set_ylabel(\"True label\")\n",
    "        axes[i].set_xticklabels(['True', 'False'])\n",
    "        axes[i].set_yticklabels(['True', 'False'])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 다중 레이블 오차 행렬 시각화\n",
    "plot_multilabel_confusion_matrix(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0eb5693",
   "metadata": {},
   "source": [
    "# Save models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "id": "38c44f0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Bench Press\n",
    "with open('benchpress_model_deep.pkl', 'wb') as f:\n",
    "    pickle.dump(bench_model, f)\n",
    "\n",
    "# Squat\n",
    "with open('squat_model_deep.pkl', 'wb') as f:\n",
    "    pickle.dump(squat_model, f)\n",
    "\n",
    "# Deadlift\n",
    "with open('deadlift_model_deep.pkl', 'wb') as f:\n",
    "    pickle.dump(deadlift_model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "id": "eca7f824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([239, 132])"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bench_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "ffeba6c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([232, 132])"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squat_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "id": "923f2a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([285, 132])"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deadlift_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc644d48",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PyTorch",
   "language": "python",
   "name": "pytorch_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}